EU-AIMS ADI-R Subtyping Connectivity Analysis

easypackages::libraries(c("here","ggplot2","nlme","readxl","matlabr","circlize","scico"))
source("/Users/mlombardo/Dropbox/GitHubRepos/utils/cohens_d.R")
source("/Users/mlombardo/Dropbox/R/Repfunctionspack6.R")
source("/Users/mlombardo/Dropbox/R/get_ggColorHue.R")
fdr_thresh = 0.05

options(matlab.path = "/Applications/MATLAB_R2019b.app/bin")

rootpath = "/Users/mlombardo/Dropbox/euaims/data/adir"
datapath = here("data")
codepath = here("code")
resultpath = here("results")
plotpath = here("plots")

Run the MATLAB script that estimates the partial correlations

RUNMATLAB = FALSE

if (RUNMATLAB){
  # z = 0.5
  code2run = sprintf("cd %s; estimateConnectivity_z05('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 0.6
  code2run = sprintf("cd %s; estimateConnectivity_z06('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 0.7
  code2run = sprintf("cd %s; estimateConnectivity_z07('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)

  # z = 0.8
  code2run = sprintf("cd %s; estimateConnectivity_z08('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 0.9
  code2run = sprintf("cd %s; estimateConnectivity_z09('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 1
  code2run = sprintf("cd %s; estimateConnectivity_z1('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
}

Main analysis - Z = 0.5

# Z threshold
z_thresh = 0.5

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")


vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
  
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")

    
    #--------------------------------------------------------------------------
    # Discovery 
    
    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC06 IC01_IC06                   1.8413429                0.067533513
## IC01_IC17 IC01_IC17                   1.5666812                0.119282487
## IC03_IC12 IC03_IC12                   2.8401768                0.005131071
## IC03_IC13 IC03_IC13                   2.2147343                0.028276159
## IC04_IC12 IC04_IC12                   1.3828595                0.168749492
## IC07_IC13 IC07_IC13                  -2.8083624                0.005637955
## IC08_IC13 IC08_IC13                  -0.3378935                0.735912773
## IC11_IC17 IC11_IC17                   1.8988090                0.059497073
## IC12_IC17 IC12_IC17                   1.2111391                0.227734188
## IC13_IC14 IC13_IC14                  -1.8206843                0.070634676
## IC14_IC16 IC14_IC16                  -3.1379365                0.002046295
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC06              -0.31589407                  44.16421
## IC01_IC17              -0.31085336                  42.95287
## IC03_IC12              -0.52491355                  36.32295
## IC03_IC13              -0.40328866                 -66.73545
## IC04_IC12              -0.27533235                  20.61361
## IC07_IC13               0.46427323                 -35.40795
## IC08_IC13               0.05698519                 -82.82008
## IC11_IC17              -0.36550529                 114.54788
## IC12_IC17              -0.22269681                  53.46934
## IC13_IC14               0.32622066                -183.60601
## IC14_IC16               0.59459741                -186.70432
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC06                  62.42476                  2.6363624
## IC01_IC17                  61.21342                  2.3276059
## IC03_IC12                  54.58350                  2.5555425
## IC03_IC13                 -48.47490                  1.3725889
## IC04_IC12                  38.87416                  2.6556596
## IC07_IC13                 -17.14740                 -2.9826343
## IC08_IC13                 -64.55952                 -0.2485671
## IC11_IC17                 132.80843                 -0.1204165
## IC12_IC17                  71.72989                  1.1570772
## IC13_IC14                -165.34546                 -2.2360710
## IC14_IC16                -168.44377                 -2.5131329
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC06               0.009216116             -0.44305525
## IC01_IC17               0.021201529             -0.40944267
## IC03_IC12               0.011546013             -0.44816623
## IC03_IC13               0.171825394             -0.26660805
## IC04_IC12               0.008726603             -0.48080671
## IC07_IC13               0.003311324              0.53282941
## IC08_IC13               0.804018491              0.03831356
## IC11_IC17               0.904306204              0.01374379
## IC12_IC17               0.248987131             -0.21085918
## IC13_IC14               0.026747795              0.37904537
## IC14_IC16               0.012968756              0.43149673
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC06                60.451246                 78.97682       18.7576866
## IC01_IC17                 8.734708                 27.26029        8.9771527
## IC03_IC12                29.532908                 48.05849       16.2822748
## IC03_IC13              -129.435841               -110.91026        1.4112741
## IC04_IC12                32.691545                 51.21712       15.7607913
## IC07_IC13               -95.441355                -76.91578       52.9392129
## IC08_IC13              -114.380991                -95.85541        0.7067550
## IC11_IC17               137.674609                156.20019        0.2381482
## IC12_IC17                26.012760                 44.53834        1.3365816
## IC13_IC14              -165.132193               -146.60661        7.9407844
## IC14_IC16              -169.847536               -151.32196       13.1868082
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC06                  0.7572340               0.449842834
## IC01_IC17                  0.4593805               0.646483354
## IC03_IC12                  2.0187698               0.044909445
## IC03_IC13                  2.6093897               0.009788873
## IC04_IC12                  1.7982573               0.073715378
## IC07_IC13                 -1.6891351               0.092825290
## IC08_IC13                  1.1404445               0.255529170
## IC11_IC17                  1.8352267               0.068027294
## IC12_IC17                  2.3199351               0.021401223
## IC13_IC14                 -2.0447737               0.042249033
## IC14_IC16                 -2.4362432               0.015757921
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC06             -0.08383745                 15.16530
## IC01_IC17             -0.05621161                 90.02013
## IC03_IC12             -0.28401316                 50.42322
## IC03_IC13             -0.36665348               -105.62195
## IC04_IC12             -0.27682551                 26.79304
## IC07_IC13              0.22091396                -58.55765
## IC08_IC13             -0.17852219               -110.99064
## IC11_IC17             -0.25174213                148.75789
## IC12_IC17             -0.34180496                 56.50485
## IC13_IC14              0.29431425               -234.25081
## IC14_IC16              0.33880867               -236.85212
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC06                 34.80330                -0.1760962
## IC01_IC17                109.65813                 2.9866349
## IC03_IC12                 70.06122                 1.4449144
## IC03_IC13                -85.98396                 2.7443254
## IC04_IC12                 46.43104                 1.3442740
## IC07_IC13                -38.91965                -3.5391276
## IC08_IC13                -91.35264                 2.7217617
## IC11_IC17                168.39589                 2.3422629
## IC12_IC17                 76.14285                 3.4007708
## IC13_IC14               -214.61282                -2.5826298
## IC14_IC16               -217.21412                -1.9903581
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC06             0.8604001473             0.03151866
## IC01_IC17             0.0031806569            -0.39211560
## IC03_IC12             0.1500787616            -0.20805161
## IC03_IC13             0.0066267129            -0.38565128
## IC04_IC12             0.1804136252            -0.18329723
## IC07_IC13             0.0005013321             0.49701868
## IC08_IC13             0.0070784513            -0.38673454
## IC11_IC17             0.0201706598            -0.31974650
## IC12_IC17             0.0008142816            -0.44419738
## IC13_IC14             0.0105337161             0.36790730
## IC14_IC16             0.0479411986             0.29499465
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC06                64.05820                83.84810       0.5705348
## IC01_IC17                22.87360                42.66350      11.9310339
## IC03_IC12                15.20130                34.99120       1.8168802
## IC03_IC13              -164.62530              -144.83539      28.4982733
## IC04_IC12                41.27765                61.06755       1.6309456
## IC07_IC13               -92.10527               -72.31536     139.4856221
## IC08_IC13              -106.02197               -86.23206      14.8551875
## IC11_IC17               171.15950               190.94940      10.0034088
## IC12_IC17                18.99499                38.78489     151.7218503
## IC13_IC14              -235.90005              -216.11015      17.6910070
## IC14_IC16              -221.89274              -202.10284       4.7217566
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC06                         0.96044794                         0.3389555
## IC01_IC17                         0.96148792                         0.3384350
## IC03_IC12                         1.11565258                         0.2670245
## IC03_IC13                         0.07808261                         0.9379055
## IC04_IC12                        -0.20532914                         0.8376979
## IC07_IC13                        -0.99012090                         0.3243079
## IC08_IC13                        -1.11081930                         0.2690905
## IC11_IC17                         0.40321789                         0.6875778
## IC12_IC17                        -0.61611145                         0.5391055
## IC13_IC14                        -0.22061138                         0.8258077
## IC14_IC16                        -1.31139157                         0.1924812
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC06                    -0.228902618                        52.107351
## IC01_IC17                    -0.204018229                        84.797584
## IC03_IC12                    -0.217240446                        49.153699
## IC03_IC13                    -0.044318968                       -29.145006
## IC04_IC12                     0.009801726                        65.683694
## IC07_IC13                     0.235977357                        -9.320828
## IC08_IC13                     0.225051320                       -36.378068
## IC11_IC17                    -0.109625877                        78.425507
## IC12_IC17                     0.122934322                        42.704912
## IC13_IC14                     0.029911084                      -156.737603
## IC14_IC16                     0.234862038                      -117.257104
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC06                        68.418344                        2.67891056
## IC01_IC17                       101.108577                       -0.18684414
## IC03_IC12                        65.464693                        1.24865139
## IC03_IC13                       -12.834013                       -0.68326938
## IC04_IC12                        81.994688                        1.44574773
## IC07_IC13                         6.990165                        0.01194164
## IC08_IC13                       -20.067075                       -2.27939870
## IC11_IC17                        94.736501                       -2.06457055
## IC12_IC17                        59.015905                       -1.25724278
## IC13_IC14                      -140.426610                        0.16737482
## IC14_IC16                      -100.946110                       -0.57184903
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC06                       0.00843168                   -0.523643178
## IC01_IC17                       0.85210101                    0.008086164
## IC03_IC12                       0.21424351                   -0.215835437
## IC03_IC13                       0.49576481                    0.121798397
## IC04_IC12                       0.15087648                   -0.277527405
## IC07_IC13                       0.99049218                   -0.009221918
## IC08_IC13                       0.02442518                    0.420033071
## IC11_IC17                       0.04113654                    0.357550945
## IC12_IC17                       0.21112764                    0.216749249
## IC13_IC14                       0.86735923                    0.031441376
## IC14_IC16                       0.56850289                    0.148429035
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC06                        30.54137                       47.365500
## IC01_IC17                        25.18611                       42.010238
## IC03_IC12                        63.06124                       79.885368
## IC03_IC13                       -78.79223                      -61.968101
## IC04_IC12                        46.26644                       63.090570
## IC07_IC13                       -19.04980                       -2.225672
## IC08_IC13                       -48.77397                      -31.949846
## IC11_IC17                       103.26047                      120.084592
## IC12_IC17                        43.91449                       60.738618
## IC13_IC14                      -139.35921                     -122.535080
## IC14_IC16                      -139.67194                     -122.847810
##           SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC06                    12.0374937
## IC01_IC17                     0.4961466
## IC03_IC12                     1.4947995
## IC03_IC13                     0.7565176
## IC04_IC12                     1.0152675
## IC07_IC13                     0.5258595
## IC08_IC13                     6.6650092
## IC11_IC17                     1.3195485
## IC12_IC17                     1.3860308
## IC13_IC14                     0.6703009
## IC14_IC16                     0.6902873
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC06 IC01_IC06       18.7576866       0.5705348
## IC01_IC17 IC01_IC17        8.9771527      11.9310339
## IC03_IC12 IC03_IC12       16.2822748       1.8168802
## IC03_IC13 IC03_IC13        1.4112741      28.4982733
## IC04_IC12 IC04_IC12       15.7607913       1.6309456
## IC07_IC13 IC07_IC13       52.9392129     139.4856221
## IC08_IC13 IC08_IC13        0.7067550      14.8551875
## IC11_IC17 IC11_IC17        0.2381482      10.0034088
## IC12_IC17 IC12_IC17        1.3365816     151.7218503
## IC13_IC14 IC13_IC14        7.9407844      17.6910070
## IC14_IC16 IC14_IC16       13.1868082       4.7217566
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.6

# Z threshold
z_thresh = 0.6

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis 
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC06 IC01_IC06                  1.66254822                0.098348538
## IC01_IC17 IC01_IC17                  0.79415989                0.428271355
## IC03_IC12 IC03_IC12                  2.48017473                0.014160627
## IC03_IC13 IC03_IC13                  2.09875540                0.037398931
## IC05_IC11 IC05_IC11                 -2.66151777                0.008567424
## IC07_IC13 IC07_IC13                 -2.89348322                0.004337998
## IC08_IC13 IC08_IC13                  0.07297889                0.941913500
## IC12_IC17 IC12_IC17                  0.84206122                0.401002769
## IC13_IC14 IC13_IC14                 -1.46699958                0.144326819
## IC14_IC16 IC14_IC16                 -2.72038159                0.007237421
## IC17_IC18 IC17_IC18                  2.77030543                0.006259178
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC06              -0.25852192                  45.21375
## IC01_IC17              -0.14471172                  55.67809
## IC03_IC12              -0.41947863                  37.64433
## IC03_IC13              -0.35116166                 -80.29124
## IC05_IC11               0.43039717                 148.39503
## IC07_IC13               0.44878483                 -45.20098
## IC08_IC13              -0.01507376                 -91.87089
## IC12_IC17              -0.14260614                  65.76743
## IC13_IC14               0.23486852                -198.80248
## IC14_IC16               0.47053788                -195.47539
## IC17_IC18              -0.46470551                  62.89701
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC06                  63.84943                  2.4961847
## IC01_IC17                  74.31376                  1.8472649
## IC03_IC12                  56.28000                  2.5343524
## IC03_IC13                 -61.65557                  1.8102804
## IC05_IC11                 167.03070                  0.5689963
## IC07_IC13                 -26.56531                 -2.7969355
## IC08_IC13                 -73.23522                 -0.2070050
## IC12_IC17                  84.40310                  1.2522290
## IC13_IC14                -180.16681                 -2.8143652
## IC14_IC16                -176.83971                 -2.6525964
## IC17_IC18                  81.53268                  2.1449450
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC06               0.013529643             -0.39322901
## IC01_IC17               0.066488695             -0.29199258
## IC03_IC12               0.012190809             -0.41328120
## IC03_IC13               0.072060812             -0.32638966
## IC05_IC11               0.570127679             -0.12044979
## IC07_IC13               0.005768569              0.46791755
## IC08_IC13               0.836259690              0.01988449
## IC12_IC17               0.212247854             -0.22309547
## IC13_IC14               0.005478483              0.45447878
## IC14_IC16               0.008762645              0.41803385
## IC17_IC18               0.033411491             -0.33705288
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC06                 57.52452                 76.33931      12.95832239
## IC01_IC17                 19.72037                 38.53516       2.93775710
## IC03_IC12                 33.17553                 51.99032      16.46792960
## IC03_IC13               -126.63378               -107.81898       3.44278123
## IC05_IC11                113.94220                132.75699       0.05892928
## IC07_IC13               -101.29119                -82.47640      31.97489637
## IC08_IC13               -109.76437                -90.94958       0.69791010
## IC12_IC17                 22.57417                 41.38896       1.47226933
## IC13_IC14               -178.33909               -159.52430      22.59791876
## IC14_IC16               -185.63020               -166.81541      21.98868108
## IC17_IC18                 32.87683                 51.69162       6.00923248
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC06                  0.8808227               0.379556678
## IC01_IC17                  0.6160951               0.538589035
## IC03_IC12                  2.1268975               0.034753760
## IC03_IC13                  2.7839134               0.005928823
## IC05_IC11                 -1.9327690               0.054791642
## IC07_IC13                 -1.5816917               0.115428095
## IC08_IC13                  1.0485194               0.295767220
## IC12_IC17                  2.1611994               0.031965059
## IC13_IC14                 -2.2745029               0.024083487
## IC14_IC16                 -2.4084524               0.017001826
## IC17_IC18                  3.1078386               0.002182117
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC06             -0.11238664                 12.83930
## IC01_IC17             -0.09209342                 84.31261
## IC03_IC12             -0.31057674                 51.05153
## IC03_IC13             -0.39836167                -99.21376
## IC05_IC11              0.25989884                176.33365
## IC07_IC13              0.20549220                -52.56735
## IC08_IC13             -0.16192331               -106.51183
## IC12_IC17             -0.32432466                 58.92219
## IC13_IC14              0.33697796               -224.96543
## IC14_IC16              0.34429162               -230.99690
## IC17_IC18             -0.44888984                 49.70195
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC06                 32.28978                -0.2422423
## IC01_IC17                103.76309                 3.0525970
## IC03_IC12                 70.50201                 1.3701996
## IC03_IC13                -79.76328                 2.7426661
## IC05_IC11                195.78413                -2.3497592
## IC07_IC13                -33.11686                -3.6032261
## IC08_IC13                -87.06135                 2.8430103
## IC12_IC17                 78.37267                 3.3915343
## IC13_IC14               -205.51495                -2.1997532
## IC14_IC16               -211.54642                -1.9268669
## IC17_IC18                 69.15243                 2.4021744
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC06             0.8088550619             0.04498497
## IC01_IC17             0.0025955555            -0.42213806
## IC03_IC12             0.1722499778            -0.20161752
## IC03_IC13             0.0066803760            -0.39049183
## IC05_IC11             0.0198173583             0.34354024
## IC07_IC13             0.0004018150             0.52570319
## IC08_IC13             0.0049604164            -0.41342284
## IC12_IC17             0.0008463575            -0.45816563
## IC13_IC14             0.0290371423             0.31371071
## IC14_IC16             0.0554959908             0.28752789
## IC17_IC18             0.0172660650            -0.34222028
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC06               66.916997                86.49314       0.5253045
## IC01_IC17               25.560412                45.13655      15.9854247
## IC03_IC12                9.693625                29.26977       1.5382473
## IC03_IC13             -156.466683              -136.89054      28.3825036
## IC05_IC11              152.597179               172.17332      10.3605754
## IC07_IC13              -86.723771               -67.14763     146.3930844
## IC08_IC13             -109.761729               -90.18559      17.2332532
## IC12_IC17               20.818236                40.39438     134.8148356
## IC13_IC14             -225.270741              -205.69460       7.6815398
## IC14_IC16             -208.133690              -188.55755       4.1524626
## IC17_IC18               42.412634                61.98877      10.5815455
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC06                          0.6727654                         0.5024700
## IC01_IC17                          0.2166683                         0.8288575
## IC03_IC12                          0.5127720                         0.6091118
## IC03_IC13                         -0.3369085                         0.7368108
## IC05_IC11                         -0.8539263                         0.3949525
## IC07_IC13                         -1.1602716                         0.2483837
## IC08_IC13                         -0.7590929                         0.4493775
## IC12_IC17                         -0.9263115                         0.3562583
## IC13_IC14                          0.6753604                         0.5008265
## IC14_IC16                         -0.7559547                         0.4512494
## IC17_IC18                          0.1960251                         0.8449426
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC06                     -0.15079098                         51.26289
## IC01_IC17                     -0.03899752                         88.17773
## IC03_IC12                     -0.09246625                         50.34586
## IC03_IC13                      0.04528777                        -35.35772
## IC05_IC11                      0.15905282                        121.65789
## IC07_IC13                      0.23011161                        -13.47202
## IC08_IC13                      0.14181483                        -40.66506
## IC12_IC17                      0.17389515                         57.45723
## IC13_IC14                     -0.12715793                       -162.71368
## IC14_IC16                      0.12024751                       -119.23613
## IC17_IC18                     -0.04217918                         12.33623
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC06                        67.784432                        2.45688686
## IC01_IC17                       104.699270                       -0.79020048
## IC03_IC12                        66.867405                        1.18290539
## IC03_IC13                       -18.836182                       -0.33453564
## IC05_IC11                       138.179433                        2.81601484
## IC07_IC13                         3.049524                        0.45466195
## IC08_IC13                       -24.143519                       -2.33959265
## IC12_IC17                        73.978776                       -1.27310054
## IC13_IC14                      -146.192138                       -0.77863750
## IC14_IC16                      -102.714592                       -0.64941699
## IC17_IC18                        28.857776                       -0.02695177
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC06                      0.015444928                    -0.47912367
## IC01_IC17                      0.430969482                     0.13465623
## IC03_IC12                      0.239184975                    -0.19926175
## IC03_IC13                      0.738559397                     0.05855422
## IC05_IC11                      0.005686161                    -0.45450933
## IC07_IC13                      0.650173873                    -0.08947701
## IC08_IC13                      0.020957137                     0.42164800
## IC12_IC17                      0.205443187                     0.22235722
## IC13_IC14                      0.437725972                     0.16660735
## IC14_IC16                      0.517309992                     0.13193024
## IC17_IC18                      0.978542972                     0.01548788
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC06                        30.99300                       47.866102
## IC01_IC17                        37.21348                       54.086587
## IC03_IC12                        62.77505                       79.648157
## IC03_IC13                       -68.36771                      -51.494604
## IC05_IC11                       110.35613                      127.229240
## IC07_IC13                       -19.72100                       -2.847891
## IC08_IC13                       -49.52959                      -32.656479
## IC12_IC17                        43.03561                       59.908720
## IC13_IC14                      -142.32967                     -125.456560
## IC14_IC16                      -141.93115                     -125.058042
## IC17_IC18                        45.16982                       62.042924
##           SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC06                     6.4250267
## IC01_IC17                     0.7412616
## IC03_IC12                     1.2623906
## IC03_IC13                     0.7355969
## IC05_IC11                     1.2656620
## IC07_IC13                     0.3979671
## IC08_IC13                     5.7544099
## IC12_IC17                     1.5244974
## IC13_IC14                     0.5567477
## IC14_IC16                     0.8553861
## IC17_IC18                     0.6872281
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC06 IC01_IC06      12.95832239       0.5253045
## IC01_IC17 IC01_IC17       2.93775710      15.9854247
## IC03_IC12 IC03_IC12      16.46792960       1.5382473
## IC03_IC13 IC03_IC13       3.44278123      28.3825036
## IC05_IC11 IC05_IC11       0.05892928      10.3605754
## IC07_IC13 IC07_IC13      31.97489637     146.3930844
## IC08_IC13 IC08_IC13       0.69791010      17.2332532
## IC12_IC17 IC12_IC17       1.47226933     134.8148356
## IC13_IC14 IC13_IC14      22.59791876       7.6815398
## IC14_IC16 IC14_IC16      21.98868108       4.1524626
## IC17_IC18 IC17_IC18       6.00923248      10.5815455
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.7

# Z threshold
z_thresh = 0.7

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC03_IC12 IC03_IC12                   2.3221454                0.021417719
## IC03_IC13 IC03_IC13                   2.0756019                0.039445282
## IC05_IC06 IC05_IC06                  -1.0880070                0.278141201
## IC07_IC13 IC07_IC13                  -2.8899015                0.004359289
## IC08_IC13 IC08_IC13                   0.5614606                0.575227364
## IC12_IC17 IC12_IC17                   0.9643378                0.336254474
## IC13_IC14 IC13_IC14                  -1.9731060                0.050114214
## IC17_IC18 IC17_IC18                   3.0818332                0.002402717
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC03_IC12              -0.37938636                  33.60332
## IC03_IC13              -0.33207572                 -92.01983
## IC05_IC06               0.17355520                 144.86564
## IC07_IC13               0.43340407                 -45.05961
## IC08_IC13              -0.08830724                 -97.68590
## IC12_IC17              -0.15543100                  61.97986
## IC13_IC14               0.30097111                -213.99985
## IC17_IC18              -0.48912800                  60.90796
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC03_IC12                  52.52307                  2.5755098
## IC03_IC13                 -73.10008                  1.8029660
## IC05_IC06                 163.78539                 -2.5340428
## IC07_IC13                 -26.13986                 -3.2756600
## IC08_IC13                 -78.76615                  0.4297624
## IC12_IC17                  80.89961                  0.9077748
## IC13_IC14                -195.08010                 -2.9243534
## IC17_IC18                  79.82770                  1.9834594
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC03_IC12               0.010830672             -0.40226600
## IC03_IC13               0.073104214             -0.31025425
## IC05_IC06               0.012147823              0.38582603
## IC07_IC13               0.001269619              0.50957403
## IC08_IC13               0.667894165             -0.08253222
## IC12_IC17               0.365238501             -0.16341147
## IC13_IC14               0.003906066              0.44677373
## IC17_IC18               0.048871280             -0.29935754
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC03_IC12                 28.13199                 47.28974        18.109006
## IC03_IC13               -142.87159               -123.71385         3.394453
## IC05_IC06                121.03319                140.19093        10.257489
## IC07_IC13               -106.48364                -87.32590       126.898503
## IC08_IC13                -99.99569                -80.83795         0.757630
## IC12_IC17                 26.27354                 45.43128         1.047415
## IC13_IC14               -198.61959               -179.46185        38.176593
## IC17_IC18                 39.19986                 58.35761         3.445757
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC03_IC12                 2.08229921               0.038729153
## IC03_IC13                 2.77204527               0.006156424
## IC05_IC06                 0.09497558               0.924439872
## IC07_IC13                -1.52102284               0.130008812
## IC08_IC13                 0.74973991               0.454390144
## IC12_IC17                 2.02740553               0.044095734
## IC13_IC14                -2.23495975               0.026649776
## IC17_IC18                 2.90490403               0.004134334
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC03_IC12             -0.31137544                 54.36135
## IC03_IC13             -0.40294746                -92.31312
## IC05_IC06             -0.01273963                126.35970
## IC07_IC13              0.20454946                -56.21310
## IC08_IC13             -0.12995742               -101.94828
## IC12_IC17             -0.31061496                 62.05198
## IC13_IC14              0.33615135               -213.21721
## IC17_IC18             -0.43594941                 51.03480
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC03_IC12                 73.65096                  1.444407
## IC03_IC13                -73.02350                  2.780506
## IC05_IC06                145.64932                 -3.140018
## IC07_IC13                -36.92349                 -3.053138
## IC08_IC13                -82.65867                  2.733688
## IC12_IC17                 81.34159                  3.562849
## IC13_IC14               -193.92759                 -1.854706
## IC17_IC18                 70.32441                  2.486167
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC03_IC12             0.1503440011             -0.2113858
## IC03_IC13             0.0059982978             -0.4022988
## IC05_IC06             0.0019717131              0.4609681
## IC07_IC13             0.0026041380              0.4708069
## IC08_IC13             0.0068813790             -0.4078235
## IC12_IC17             0.0004684346             -0.5011120
## IC13_IC14             0.0652558885              0.2808212
## IC17_IC18             0.0138140386             -0.3457352
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC03_IC12                15.58339                34.93787        1.796769
## IC03_IC13              -145.79806              -126.44358       31.502747
## IC05_IC06               120.12686               139.48134        6.699426
## IC07_IC13               -84.18131               -64.82683       38.318912
## IC08_IC13              -102.15276               -82.79828       10.640599
## IC12_IC17                16.40916                35.76364      190.471531
## IC13_IC14              -210.19769              -190.84320        3.745154
## IC17_IC18                36.47313                55.82761       14.191801
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC03_IC12                          0.3190830                         0.7502375
## IC03_IC13                         -0.4926518                         0.6231898
## IC05_IC06                         -1.0641113                         0.2894883
## IC07_IC13                         -1.1850184                         0.2384323
## IC08_IC13                         -0.2468525                         0.8054587
## IC12_IC17                         -0.8468887                         0.3988010
## IC13_IC14                          0.2802622                         0.7797756
## IC17_IC18                          0.2959209                         0.7678196
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC03_IC12                     -0.04668964                         49.95491
## IC03_IC13                      0.07778101                        -40.12727
## IC05_IC06                      0.19161003                        105.10159
## IC07_IC13                      0.22658887                        -16.89676
## IC08_IC13                      0.04135501                        -43.14922
## IC12_IC17                      0.15370190                         56.69659
## IC13_IC14                     -0.05724727                       -164.95111
## IC17_IC18                     -0.07916051                         10.80373
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC03_IC12                       66.6296490                        1.14189559
## IC03_IC13                      -23.4525241                       -0.50949046
## IC05_IC06                      121.7763292                        0.54424508
## IC07_IC13                       -0.2220195                       -0.08776844
## IC08_IC13                      -26.4744765                       -1.74811838
## IC12_IC17                       73.3713349                       -1.75752310
## IC13_IC14                     -148.2763723                       -0.85599717
## IC17_IC18                       27.4784667                       -0.07494163
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC03_IC12                       0.25571552                    -0.17433072
## IC03_IC13                       0.61132136                     0.09533090
## IC05_IC06                       0.58725809                    -0.07039771
## IC07_IC13                       0.93020345                     0.00370345
## IC08_IC13                       0.08293872                     0.29941140
## IC12_IC17                       0.08131659                     0.31020269
## IC13_IC14                       0.39366383                     0.17936131
## IC17_IC18                       0.94038290                     0.04383111
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC03_IC12                        62.47247                       79.490164
## IC03_IC13                       -73.47254                      -56.454847
## IC05_IC06                       108.47343                      125.491121
## IC07_IC13                       -21.93991                       -4.922215
## IC08_IC13                       -35.11306                      -18.095367
## IC12_IC17                        43.21120                       60.228892
## IC13_IC14                      -147.24087                     -130.223174
## IC17_IC18                        45.73968                       62.757368
##           SCequalRRB_vs_SCoverRRB.repBF
## IC03_IC12                     1.1392680
## IC03_IC13                     0.7924462
## IC05_IC06                     0.4206097
## IC07_IC13                     0.5118080
## IC08_IC13                     1.8559685
## IC12_IC17                     2.6666260
## IC13_IC14                     0.7316458
## IC17_IC18                     0.6739529
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC03_IC12 IC03_IC12        18.109006        1.796769
## IC03_IC13 IC03_IC13         3.394453       31.502747
## IC05_IC06 IC05_IC06        10.257489        6.699426
## IC07_IC13 IC07_IC13       126.898503       38.318912
## IC08_IC13 IC08_IC13         0.757630       10.640599
## IC12_IC17 IC12_IC17         1.047415      190.471531
## IC13_IC14 IC13_IC14        38.176593        3.745154
## IC17_IC18 IC17_IC18         3.445757       14.191801
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.8

# Z threshold
z_thresh = 0.8

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17                   1.2562834                0.210659538
## IC03_IC12 IC03_IC12                   2.4428286                0.015549238
## IC03_IC13 IC03_IC13                   1.7104317                0.088928359
## IC04_IC12 IC04_IC12                   1.4303494                0.154369816
## IC05_IC06 IC05_IC06                  -1.2455654                0.214561048
## IC07_IC13 IC07_IC13                  -2.5605860                0.011279614
## IC08_IC13 IC08_IC13                   1.1631086                0.246342333
## IC08_IC18 IC08_IC18                  -0.4384108                0.661620105
## IC12_IC17 IC12_IC17                   0.8173553                0.414818108
## IC13_IC14 IC13_IC14                  -1.9140013                0.057224312
## IC14_IC20 IC14_IC20                   0.6923233                0.489636515
## IC17_IC18 IC17_IC18                   3.0803749                0.002395653
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17              -0.20835801                  54.78516
## IC03_IC12              -0.36186591                  31.06959
## IC03_IC13              -0.25771857                 -95.55475
## IC04_IC12              -0.22557859                  14.93006
## IC05_IC06               0.18706974                 151.49343
## IC07_IC13               0.37140570                 -51.87107
## IC08_IC13              -0.17850241                 -99.99358
## IC08_IC18               0.08530723                 -69.03342
## IC12_IC17              -0.12489700                  65.01258
## IC13_IC14               0.27458970                -224.28098
## IC14_IC20              -0.10607496                -183.73910
## IC17_IC18              -0.46164136                  57.02774
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17                  74.00920                   2.637062
## IC03_IC12                  50.29363                   2.373383
## IC03_IC13                 -76.33071                   1.432138
## IC04_IC12                  34.15410                   2.491532
## IC05_IC06                 170.71747                  -2.976618
## IC07_IC13                 -32.64703                  -3.293250
## IC08_IC13                 -80.76954                   0.166787
## IC08_IC18                 -49.80938                  -1.436088
## IC12_IC17                  84.23662                   1.497541
## IC13_IC14                -205.05694                  -3.158892
## IC14_IC20                -164.51506                   2.833198
## IC17_IC18                  76.25178                   2.484298
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17               0.009089954             -0.38393257
## IC03_IC12               0.018673812             -0.35897661
## IC03_IC13               0.153829120             -0.24667367
## IC04_IC12               0.013619095             -0.39127290
## IC05_IC06               0.003313159              0.43949169
## IC07_IC13               0.001191118              0.49542376
## IC08_IC13               0.867723818             -0.04331143
## IC08_IC18               0.152703453              0.21022948
## IC12_IC17               0.135994384             -0.24516424
## IC13_IC14               0.001856199              0.47248954
## IC14_IC20               0.005131445             -0.41531301
## IC17_IC18               0.013889346             -0.36786494
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17                 12.67722                 31.99935       13.6829890
## IC03_IC12                 27.45724                 46.77938       11.3486699
## IC03_IC13               -148.63220               -129.31006        1.9106092
## IC04_IC12                 38.31937                 57.64150       11.5035145
## IC05_IC06                122.45108                141.77322       26.2974372
## IC07_IC13               -113.06599                -93.74386      123.1232890
## IC08_IC13               -105.24428                -85.92214        0.5539346
## IC08_IC18                -84.92466                -65.60252        1.5428038
## IC12_IC17                 38.56869                 57.89083        1.9229706
## IC13_IC14               -207.76974               -188.44760       64.1224204
## IC14_IC20               -202.01336               -182.69122       11.9205026
## IC17_IC18                 38.51395                 57.83609       13.4117521
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17                 0.35605317               0.722234562
## IC03_IC12                 2.02381035               0.044528297
## IC03_IC13                 3.26518020               0.001318759
## IC04_IC12                 2.10298661               0.036913618
## IC05_IC06                 0.07101364               0.943468925
## IC07_IC13                -1.64686267               0.101401555
## IC08_IC13                 0.40705983               0.684467243
## IC08_IC18                 0.55646363               0.578613028
## IC12_IC17                 2.38451605               0.018183591
## IC13_IC14                -2.47530591               0.014275049
## IC14_IC20                 1.94782403               0.053054468
## IC17_IC18                 2.91480522               0.004030075
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17            -0.048088067                 82.28289
## IC03_IC12            -0.316928373                 55.19052
## IC03_IC13            -0.501232848                -88.69614
## IC04_IC12            -0.336009224                 31.19146
## IC05_IC06            -0.008495749                123.84602
## IC07_IC13             0.224578577                -50.70787
## IC08_IC13            -0.067864543                -98.86840
## IC08_IC18            -0.101545823                -44.95714
## IC12_IC17            -0.375226524                 57.40837
## IC13_IC14             0.383416959               -203.07557
## IC14_IC20            -0.312012136               -211.78360
## IC17_IC18            -0.453810343                 54.90991
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17                101.33979                 2.6506078
## IC03_IC12                 74.24742                 1.5986018
## IC03_IC13                -69.63924                 3.1708220
## IC04_IC12                 50.24836                 0.7792706
## IC05_IC06                142.90292                -2.6916133
## IC07_IC13                -31.65097                -3.0726614
## IC08_IC13                -79.81150                 3.1340408
## IC08_IC18                -25.90024                 2.6504559
## IC12_IC17                 76.46526                 3.1698860
## IC13_IC14               -184.01867                -1.4323553
## IC14_IC20               -192.72671                 1.3583569
## IC17_IC18                 73.96681                 1.9481312
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17              0.008763035             -0.3750485
## IC03_IC12              0.111692696             -0.2424582
## IC03_IC13              0.001791788             -0.4774934
## IC04_IC12              0.436861014             -0.1159924
## IC05_IC06              0.007792825              0.4094693
## IC07_IC13              0.002457157              0.4840844
## IC08_IC13              0.002018630             -0.4771050
## IC08_IC18              0.008766823             -0.4048714
## IC12_IC17              0.001797255             -0.4434389
## IC13_IC14              0.153805828              0.2421429
## IC14_IC20              0.176079226             -0.1886743
## IC17_IC18              0.052980770             -0.2765940
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17               30.651666                49.84265       6.2097829
## IC03_IC12               16.732425                35.92341       2.3751428
## IC03_IC13             -140.456801              -121.26582      94.2481540
## IC04_IC12               37.063246                56.25423       0.6026362
## IC05_IC06              118.941330               138.13231       3.8455586
## IC07_IC13              -78.177035               -58.98605      44.3964724
## IC08_IC13             -100.046930               -80.85595      14.1882320
## IC08_IC18             -115.995021               -96.80404       7.7427657
## IC12_IC17                5.589369                24.78035      82.9404238
## IC13_IC14             -202.934458              -183.74348       1.4573599
## IC14_IC20             -271.391838              -252.20086       1.5981694
## IC17_IC18               36.767238                55.95822       3.5793761
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17                        0.745580918                         0.4574032
## IC03_IC12                        0.129251168                         0.8973790
## IC03_IC13                       -1.430886619                         0.1551057
## IC04_IC12                       -0.636052022                         0.5259743
## IC05_IC06                       -1.142312387                         0.2556368
## IC07_IC13                       -0.628783940                         0.5307058
## IC08_IC13                        0.513002368                         0.6089087
## IC08_IC18                       -1.102566731                         0.2724590
## IC12_IC17                       -1.329752480                         0.1861646
## IC13_IC14                        0.637411946                         0.5250914
## IC14_IC20                       -0.867535821                         0.3874093
## IC17_IC18                       -0.009049277                         0.9927951
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17                     -0.13407841                         86.72773
## IC03_IC12                     -0.02224662                         48.64798
## IC03_IC13                      0.23996196                        -41.27593
## IC04_IC12                      0.10810205                         65.50228
## IC05_IC06                      0.19993686                        108.61103
## IC07_IC13                      0.14332576                        -18.34445
## IC08_IC13                     -0.10618911                        -42.84859
## IC08_IC18                      0.19085529                        -30.98909
## IC12_IC17                      0.24546514                         55.09476
## IC13_IC14                     -0.13878095                       -163.86169
## IC14_IC20                      0.16231789                        -86.55234
## IC17_IC18                     -0.01201644                         11.24587
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17                       103.502470                       -0.06145934
## IC03_IC12                        65.422719                        0.68677424
## IC03_IC13                       -24.501191                       -1.41380815
## IC04_IC12                        82.277027                        1.50373894
## IC05_IC06                       125.385776                       -0.02632118
## IC07_IC13                        -1.569709                        0.11377506
## IC08_IC13                       -26.073843                       -2.28934563
## IC08_IC18                       -14.214344                       -2.97670556
## IC12_IC17                        71.869502                       -0.95627517
## IC13_IC14                      -147.086949                       -1.08564086
## IC14_IC20                       -69.777594                        1.25149443
## IC17_IC18                        28.020611                        0.56274395
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17                      0.951093149                    0.009856602
## IC03_IC12                      0.493517753                   -0.109585142
## IC03_IC13                      0.159944262                    0.229436077
## IC04_IC12                      0.135211948                   -0.265279021
## IC05_IC06                      0.979043819                    0.028987931
## IC07_IC13                      0.909601612                   -0.026449777
## IC08_IC13                      0.023764128                    0.400753332
## IC08_IC18                      0.003509457                    0.577323507
## IC12_IC17                      0.340808976                    0.157209625
## IC13_IC14                      0.279761069                    0.244389150
## IC14_IC20                      0.213129071                   -0.247627605
## IC17_IC18                      0.574633528                   -0.088110873
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17                        36.20507                       53.222764
## IC03_IC12                        63.30585                       80.323540
## IC03_IC13                       -75.14364                      -58.125945
## IC04_IC12                        46.57862                       63.596313
## IC05_IC06                       108.75878                      125.776468
## IC07_IC13                       -21.93822                       -4.920526
## IC08_IC13                       -37.22171                      -20.204020
## IC08_IC18                       -41.80025                      -24.782563
## IC12_IC17                        45.35860                       62.376294
## IC13_IC14                      -147.68513                     -130.667440
## IC14_IC20                      -111.91215                      -94.894460
## IC17_IC18                        45.43893                       62.456623
##           SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17                     0.5938849
## IC03_IC12                     0.8222121
## IC03_IC13                     1.8938805
## IC04_IC12                     0.6964103
## IC05_IC06                     0.5096203
## IC07_IC13                     0.6128578
## IC08_IC13                     1.3497890
## IC08_IC18                    22.6621292
## IC12_IC17                     1.0623730
## IC13_IC14                     0.6032523
## IC14_IC20                     0.5002514
## IC17_IC18                     0.7571995
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17       13.6829890       6.2097829
## IC03_IC12 IC03_IC12       11.3486699       2.3751428
## IC03_IC13 IC03_IC13        1.9106092      94.2481540
## IC04_IC12 IC04_IC12       11.5035145       0.6026362
## IC05_IC06 IC05_IC06       26.2974372       3.8455586
## IC07_IC13 IC07_IC13      123.1232890      44.3964724
## IC08_IC13 IC08_IC13        0.5539346      14.1882320
## IC08_IC18 IC08_IC18        1.5428038       7.7427657
## IC12_IC17 IC12_IC17        1.9229706      82.9404238
## IC13_IC14 IC13_IC14       64.1224204       1.4573599
## IC14_IC20 IC14_IC20       11.9205026       1.5981694
## IC17_IC18 IC17_IC18       13.4117521       3.5793761
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.9

# Z threshold
z_thresh = 0.9

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17                   1.1940808                0.233991855
## IC03_IC12 IC03_IC12                   2.6603891                0.008498259
## IC03_IC13 IC03_IC13                   2.1694654                0.031336708
## IC04_IC12 IC04_IC12                   1.7519463                0.081458088
## IC05_IC06 IC05_IC06                  -1.4594704                0.146150104
## IC07_IC13 IC07_IC13                  -2.4452150                0.015423228
## IC12_IC17 IC12_IC17                   0.8543305                0.394038903
## IC13_IC14 IC13_IC14                  -1.9232790                0.055997264
## IC17_IC18 IC17_IC18                   3.1200405                0.002101385
## IC18_IC19 IC18_IC19                  -0.5166293                0.606038716
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17              -0.19649856                  52.15723
## IC03_IC12              -0.38448867                  27.18358
## IC03_IC13              -0.32427108                 -78.16961
## IC04_IC12              -0.26806180                  17.72636
## IC05_IC06               0.21443563                 152.69424
## IC07_IC13               0.34744877                 -55.36740
## IC12_IC17              -0.12711967                  63.21058
## IC13_IC14               0.27069103                -234.18404
## IC17_IC18              -0.45582958                  56.43592
## IC18_IC19               0.09370915                 -63.73547
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17                  71.54388                   2.226675
## IC03_IC12                  46.57024                   2.476757
## IC03_IC13                 -58.78296                   2.032160
## IC04_IC12                  37.11301                   2.837115
## IC05_IC06                 172.08089                  -3.332082
## IC07_IC13                 -35.98075                  -3.574820
## IC12_IC17                  82.59723                   1.979163
## IC13_IC14                -214.79739                  -3.465509
## IC17_IC18                  75.82257                   2.675990
## IC18_IC19                 -44.34882                   2.216665
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17              0.0271311934              -0.3126138
## IC03_IC12              0.0141229781              -0.3604296
## IC03_IC13              0.0435149857              -0.3146501
## IC04_IC12              0.0050411087              -0.4199090
## IC05_IC06              0.0010339977               0.4741883
## IC07_IC13              0.0004433505               0.5084838
## IC12_IC17              0.0492269786              -0.2869355
## IC13_IC14              0.0006528278               0.4889647
## IC17_IC18              0.0080947826              -0.3811268
## IC18_IC19              0.0278199145              -0.3131645
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17                 21.29519                40.963880         6.394467
## IC03_IC12                 31.70846                51.377147        14.102934
## IC03_IC13               -163.99950              -144.330817         5.332813
## IC04_IC12                 31.88404                51.552728        28.344443
## IC05_IC06                125.38211               145.050800        71.430424
## IC07_IC13               -122.31427              -102.645578       267.606852
## IC12_IC17                 34.91776                54.586447         3.643369
## IC13_IC14               -226.99247              -207.323782       143.740693
## IC17_IC18                 34.87671                54.545398        21.942334
## IC18_IC19                -12.12802                 7.540671         1.286343
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17                  0.3694011               0.712293963
## IC03_IC12                  1.8085136               0.072314838
## IC03_IC13                  2.8415347               0.005045815
## IC04_IC12                  1.7574970               0.080654558
## IC05_IC06                  0.4069436               0.684567405
## IC07_IC13                 -1.6732648               0.096136180
## IC12_IC17                  2.4172479               0.016708367
## IC13_IC14                 -2.4827350               0.014019785
## IC17_IC18                  2.8727582               0.004594296
## IC18_IC19                  1.6028123               0.110854587
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17             -0.05016234                 84.32660
## IC03_IC12             -0.28928800                 58.27217
## IC03_IC13             -0.44277156               -106.84779
## IC04_IC12             -0.29469890                 29.45273
## IC05_IC06             -0.06328016                121.77654
## IC07_IC13              0.24206035                -47.07759
## IC12_IC17             -0.39364722                 59.05240
## IC13_IC14              0.39836120               -193.50246
## IC17_IC18             -0.46048779                 55.12351
## IC18_IC19             -0.26529743                -90.29840
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17                103.21157                 3.0246998
## IC03_IC12                 77.15713                 1.4781494
## IC03_IC13                -87.96282                 2.7715486
## IC04_IC12                 48.33770                 0.2568189
## IC05_IC06                140.66150                -2.2478960
## IC07_IC13                -28.19262                -2.7046353
## IC12_IC17                 77.93737                 2.7360647
## IC13_IC14               -174.61749                -0.8767594
## IC17_IC18                 74.00848                 1.5520366
## IC18_IC19                -71.41343                 2.4794661
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17              0.002882340            -0.47748080
## IC03_IC12              0.141251361            -0.22945843
## IC03_IC13              0.006212221            -0.42581967
## IC04_IC12              0.797634723            -0.04730255
## IC05_IC06              0.025891461             0.36354360
## IC07_IC13              0.007546534             0.46198324
## IC12_IC17              0.006890468            -0.40829514
## IC13_IC14              0.381876802             0.18210254
## IC17_IC18              0.122546521            -0.23240787
## IC18_IC19              0.014150239            -0.40553482
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17                21.02652                39.87650      10.6085758
## IC03_IC12                11.82683                30.67681       2.0659636
## IC03_IC13              -126.23462              -107.38464      30.8172024
## IC04_IC12                41.33343                60.18341       0.4225573
## IC05_IC06               115.14269               133.99267       1.4785356
## IC07_IC13               -71.36100               -52.51102      19.5978233
## IC12_IC17                10.75061                29.60059      27.2602919
## IC13_IC14              -187.80986              -168.95988       0.5582649
## IC17_IC18                39.88986                58.73984       1.5571320
## IC18_IC19               -53.64883               -34.79885      12.0715553
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17                         0.66401571                        0.50797563
## IC03_IC12                         0.36326262                        0.71705866
## IC03_IC13                        -0.50468222                        0.61472323
## IC04_IC12                        -0.22444565                        0.82279873
## IC05_IC06                        -1.56335434                        0.12064724
## IC07_IC13                        -0.39972117                        0.69008451
## IC12_IC17                        -1.44404066                        0.15137743
## IC13_IC14                         0.83133199                        0.40746518
## IC17_IC18                        -0.09780341                        0.92225433
## IC18_IC19                        -1.72330241                        0.08745318
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17                    -0.120233727                        86.830643
## IC03_IC12                    -0.065028394                        48.521206
## IC03_IC13                     0.074734503                       -39.535261
## IC04_IC12                     0.031998054                        65.840061
## IC05_IC06                     0.280547796                       107.477618
## IC07_IC13                     0.100688957                       -18.125858
## IC12_IC17                     0.264861665                        54.771130
## IC13_IC14                    -0.166130409                      -164.159921
## IC17_IC18                     0.004134189                        11.221942
## IC18_IC19                     0.311698524                        -9.869156
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17                       103.605386                       -0.90741559
## IC03_IC12                        65.295950                        0.80409337
## IC03_IC13                       -22.760518                       -0.76915957
## IC04_IC12                        82.614805                        1.95474606
## IC05_IC06                       124.252361                       -0.49596276
## IC07_IC13                        -1.351114                       -0.01792758
## IC12_IC17                        71.545873                       -0.59776809
## IC13_IC14                      -147.385178                       -1.36627941
## IC17_IC18                        27.996685                        0.76908380
## IC18_IC19                         6.905587                       -0.25646378
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17                       0.36594714                    0.155535831
## IC03_IC12                       0.42288192                   -0.129230992
## IC03_IC13                       0.44326120                    0.123236336
## IC04_IC12                       0.05286326                   -0.354222483
## IC05_IC06                       0.62079862                    0.103887456
## IC07_IC13                       0.98572544                    0.004982477
## IC12_IC17                       0.55108483                    0.090009293
## IC13_IC14                       0.17432387                    0.322753186
## IC17_IC18                       0.44330600                   -0.139519833
## IC18_IC19                       0.79801776                    0.034629963
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17                        34.32432                       51.389443
## IC03_IC12                        63.28111                       80.346231
## IC03_IC13                       -75.71992                      -58.654801
## IC04_IC12                        45.34090                       62.406024
## IC05_IC06                       108.27899                      125.344108
## IC07_IC13                       -23.33855                       -6.273428
## IC12_IC17                        44.93623                       62.001355
## IC13_IC14                      -150.70409                     -133.638970
## IC17_IC18                        44.28790                       61.353024
## IC18_IC19                        37.76692                       54.832046
##           SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17                     0.5717674
## IC03_IC12                     0.9268155
## IC03_IC13                     0.9293109
## IC04_IC12                     1.4463475
## IC05_IC06                     0.5934180
## IC07_IC13                     0.6769633
## IC12_IC17                     0.6991607
## IC13_IC14                     0.5358728
## IC17_IC18                     0.7845247
## IC18_IC19                     0.4220160
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17         6.394467      10.6085758
## IC03_IC12 IC03_IC12        14.102934       2.0659636
## IC03_IC13 IC03_IC13         5.332813      30.8172024
## IC04_IC12 IC04_IC12        28.344443       0.4225573
## IC05_IC06 IC05_IC06        71.430424       1.4785356
## IC07_IC13 IC07_IC13       267.606852      19.5978233
## IC12_IC17 IC12_IC17         3.643369      27.2602919
## IC13_IC14 IC13_IC14       143.740693       0.5582649
## IC17_IC18 IC17_IC18        21.942334       1.5571320
## IC18_IC19 IC18_IC19         1.286343      12.0715553
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 1

# Z threshold
z_thresh = 1

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
tmp_df$AB_pct_severity = tmp_df$A_pct_severity + tmp_df$B_pct_severity 

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","AB_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC",
             "SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval",
             "SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC",
             "SCequalRRB_vs_SCoverRRB.repBF",
             "SCcorr_Disc.r","SCcorr_Disc.t","SCcorr_Disc.pval",
             "SCcorr_Rep.r","SCcorr_Rep.t","SCcorr_Rep.pval","SCcorr.repBF",
             "RRBcorr_Disc.r","RRBcorr_Disc.t","RRBcorr_Disc.pval",
             "RRBcorr_Rep.r","RRBcorr_Rep.t","RRBcorr_Rep.pval","RRBcorr.repBF",
             "SumSCRRB_Disc.r","SumSCRRB_Disc.t","SumSCRRB_Disc.pval",
             "SumSCRRB_Rep.r","SumSCRRB_Rep.t","SumSCRRB_Rep.pval","SumSCRRB.repBF",
             "zds_Disc.r","zds_Disc.t","zds_Disc.pval",
             "zds_Rep.r","zds_Rep.t","zds_Rep.pval","zds.repBF",
             "SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Disc.t","SumSCRRB_SCequalRRB_Disc.pval",
             "SumSCRRB_SCequalRRB_Rep.r","SumSCRRB_SCequalRRB_Rep.t",
             "SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
             "zds_SCequalRRB_Disc.r","zds_SCequalRRB_Disc.t","zds_SCequalRRB_Disc.pval",
             "zds_SCequalRRB_Rep.r","zds_SCequalRRB_Rep.t","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
             "zds_SCoverRRB_Disc.r","zds_SCoverRRB_Disc.t","zds_SCoverRRB_Disc.pval",
             "zds_SCoverRRB_Rep.r","zds_SCoverRRB_Rep.t","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
             "VinelandABC_Disc.r","VinelandABC_Disc.t","VinelandABC_Disc.pval",
             "VinelandABC_Rep.r","VinelandABC_Rep.t",
             "VinelandABC_Rep.pval","VinelandABC.repBF",
             "VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Disc.t","VinelandABC_SCequalRRB_Disc.pval",
             "VinelandABC_SCequalRRB_Rep.r","VinelandABC_SCequalRRB_Rep.t",
             "VinelandABC_SCequalRRB_Rep.pval","VinelandABC_SCequalRRB.repBF",
             "VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Disc.t","VinelandABC_SCoverRRB_Disc.pval",
             "VinelandABC_SCoverRRB_Rep.r","VinelandABC_SCoverRRB_Rep.t",
             "VinelandABC_SCoverRRB_Rep.pval","VinelandABC_SCoverRRB.repBF")
  
  
  # "vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard"
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")

    #--------------------------------------------------------------------------
    # Discovery
    
    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    
    #--------------------------------------------------------------------------
    DASD = subset(asd_df, asd_df$dataset=="Discovery")
    DASD$site = factor(DASD$site)
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SCcorr_Disc.t"] = res$tTable["A_pct_severity","t-value"]
    aovres[y_var,"SCcorr_Disc.pval"] = res$tTable["A_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$A_pct_severity)
    aovres[y_var,"SCcorr_Disc.r"] = res$estimate

    
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"RRBcorr_Disc.t"] = res$tTable["B_pct_severity","t-value"]
    aovres[y_var,"RRBcorr_Disc.pval"] = res$tTable["B_pct_severity","p-value"]

    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$B_pct_severity)
    aovres[y_var,"RRBcorr_Disc.r"] = res$estimate

    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$AB_pct_severity)
    aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate

    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_Disc.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_Disc.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$z_ds)
    aovres[y_var,"zds_Disc.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_Disc.r"] = res$estimate
    
    
    
    DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
    DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")

    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCequalRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCequalRRB_Disc.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCoverRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCoverRRB_Disc.r"] = res$estimate
    
    

    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_SCequalRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
    aovres[y_var,"SumSCRRB_SCequalRRB_Disc.r"] = res$estimate
    
    
            
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCequalRRB_Disc.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCequalRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
    aovres[y_var,"zds_SCequalRRB_Disc.r"] = res$estimate
    
  
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCoverRRB_Disc.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCoverRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
    aovres[y_var,"zds_SCoverRRB_Disc.r"] = res$estimate
      
  #     res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SCcorr_Disc.r"] = res$estimate
  #   aovres[y_var,"SCcorr_Disc.pval"] = res$p.value
  #     res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"RRBcorr_Disc.r"] = res$estimate
  #   aovres[y_var,"RRBcorr_Disc.pval"] = res$p.value
  #     res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate
  #   aovres[y_var,"SumSCRRB_Disc.pval"] = res$p.value
    n_orig = dim(DASD)[1]
    #--------------------------------------------------------------------------
    
    
    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
    
    
    #--------------------------------------------------------------------------
    DASD = subset(asd_df, asd_df$dataset=="Replication")
    DASD$site = factor(DASD$site)
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SCcorr_Rep.t"] = res$tTable["A_pct_severity","t-value"]
    aovres[y_var,"SCcorr_Rep.pval"] = res$tTable["A_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$A_pct_severity)
    aovres[y_var,"SCcorr_Rep.r"] = res$estimate

    
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"RRBcorr_Rep.t"] = res$tTable["B_pct_severity","t-value"]
    aovres[y_var,"RRBcorr_Rep.pval"] = res$tTable["B_pct_severity","p-value"]

    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$B_pct_severity)
    aovres[y_var,"RRBcorr_Rep.r"] = res$estimate

    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$AB_pct_severity)
    aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
    
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_Rep.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_Rep.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$z_ds)
    aovres[y_var,"zds_Rep.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_Rep.r"] = res$estimate
    
    
    DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
    DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")

    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCequalRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCequalRRB_Rep.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCoverRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCoverRRB_Rep.r"] = res$estimate    
    
    
    

    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_SCequalRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
    aovres[y_var,"SumSCRRB_SCequalRRB_Rep.r"] = res$estimate

            
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCequalRRB_Rep.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCequalRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
    aovres[y_var,"zds_SCequalRRB_Rep.r"] = res$estimate
    
  
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCoverRRB_Rep.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCoverRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
    aovres[y_var,"zds_SCoverRRB_Rep.r"] = res$estimate
      
    
  #     res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SCcorr_Rep.r"] = res$estimate
  #   aovres[y_var,"SCcorr_Rep.pval"] = res$p.value
  #     res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"RRBcorr_Rep.r"] = res$estimate
  #   aovres[y_var,"RRBcorr_Rep.pval"] = res$p.value
  #     res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
  #   aovres[y_var,"SumSCRRB_Rep.pval"] = res$p.value
    n_rep = dim(DASD)[1]
    #--------------------------------------------------------------------------

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

    #--------------------------------------------------------------------------
    # compute replication Bayes Factors  
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
    
    #--------------------------------------------------------------------------
    res_bf = BFSALL(tobs =aovres[y_var,"SCcorr_Disc.t"], 
                      trep = aovres[y_var,"SCcorr_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"SCcorr.repBF"] = res_bf["Replication BF","Replication 1"]
    
    res_bf = BFSALL(tobs =aovres[y_var,"RRBcorr_Disc.t"], 
                      trep = aovres[y_var,"RRBcorr_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"RRBcorr.repBF"] = res_bf["Replication BF","Replication 1"]

    res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_Disc.t"], 
                      trep = aovres[y_var,"SumSCRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"SumSCRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    res_bf = BFSALL(tobs =aovres[y_var,"zds_Disc.t"], 
                      trep = aovres[y_var,"zds_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"zds.repBF"] = res_bf["Replication BF","Replication 1"]

    
    
    res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_Disc.t"], 
                      trep = aovres[y_var,"VinelandABC_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"VinelandABC.repBF"] = res_bf["Replication BF","Replication 1"]

    
    res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"], 
                      trep = aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"SumSCRRB_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    res_bf = BFSALL(tobs =aovres[y_var,"zds_SCequalRRB_Disc.t"], 
                      trep = aovres[y_var,"zds_SCequalRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"zds_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    
    res_bf = BFSALL(tobs =aovres[y_var,"zds_SCoverRRB_Disc.t"], 
                      trep = aovres[y_var,"zds_SCoverRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"zds_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]
    
    
    res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"], 
                      trep = aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"VinelandABC_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
    
    res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"], 
                      trep = aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"VinelandABC_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    # res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SCcorr_Disc.r"], 
    #                                                         n.orig = n_orig, 
    #                                                         r.rep = aovres[y_var,"SCcorr_Rep.r"], 
    #                                                         n.rep = n_rep)
    # aovres[y_var,"SCcorr.repBF"] = res_bf["BF10"]
    # res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"RRBcorr_Disc.r"], 
    #                                                         n.orig = n_orig, 
    #                                                         r.rep = aovres[y_var,"RRBcorr_Rep.r"], 
    #                                                         n.rep = n_rep)
    # aovres[y_var,"RRBcorr.repBF"] = res_bf["BF10"]
    # res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SumSCRRB_Disc.r"], 
    #                                                         n.orig = n_orig, 
    #                                                         r.rep = aovres[y_var,"SumSCRRB_Rep.r"], 
    #                                                         n.rep = n_rep)
    # aovres[y_var,"SumSCRRB.repBF"] = res_bf["BF10"]
    #--------------------------------------------------------------------------

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    # write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask5 = aovres$SCcorr.repBF>=10
  mask6 = aovres$RRBcorr.repBF>=10
  mask7 = aovres$SumSCRRB.repBF>=10
  mask8 = aovres$zds.repBF>=10
  mask9 = aovres$zds_SCequalRRB.repBF>=10
  mask10 = aovres$zds_SCoverRRB.repBF>=10
  mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
  mask12 = aovres$VinelandABC.repBF>=10
  mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
  mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC12 IC01_IC12                 -0.12932999               0.8972333475
## IC03_IC12 IC03_IC12                  2.94092443               0.0036790286
## IC03_IC13 IC03_IC13                  2.15434545               0.0324720970
## IC03_IC18 IC03_IC18                  0.92697519               0.3551154150
## IC04_IC06 IC04_IC06                  0.63678759               0.5250296751
## IC04_IC11 IC04_IC11                 -0.02928825               0.9766654383
## IC04_IC12 IC04_IC12                  1.51303104               0.1319333691
## IC05_IC06 IC05_IC06                 -1.32603675               0.1864194275
## IC05_IC19 IC05_IC19                  1.07979275               0.2816032599
## IC07_IC13 IC07_IC13                 -2.65749362               0.0085427061
## IC08_IC11 IC08_IC11                 -0.10964393               0.9128074859
## IC08_IC20 IC08_IC20                  0.01351206               0.9892334426
## IC11_IC12 IC11_IC12                  1.30109409               0.1948018611
## IC12_IC17 IC12_IC17                  0.90500594               0.3666083763
## IC12_IC20 IC12_IC20                 -0.01609537               0.9871751958
## IC13_IC14 IC13_IC14                 -2.09077840               0.0378780563
## IC14_IC16 IC14_IC16                 -2.85242249               0.0048192212
## IC14_IC18 IC14_IC18                 -0.03279725               0.9738707030
## IC14_IC20 IC14_IC20                  1.18219798               0.2386045361
## IC15_IC17 IC15_IC17                 -1.07416328               0.2841117683
## IC17_IC18 IC17_IC18                  3.39889683               0.0008243562
## IC18_IC19 IC18_IC19                 -0.20919572               0.8345195712
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC12              0.015215795                  1.009903
## IC03_IC12             -0.411972548                 26.290817
## IC03_IC13             -0.311312998                -85.783794
## IC03_IC18             -0.129913758                -69.802261
## IC04_IC06             -0.066412895                223.253963
## IC04_IC11              0.003052759                191.732718
## IC04_IC12             -0.224794950                 17.212437
## IC05_IC06              0.188637115                152.987250
## IC05_IC19             -0.153508670                137.029142
## IC07_IC13              0.363200583                -61.276004
## IC08_IC11              0.006170505                107.690133
## IC08_IC20              0.010941672               -113.031032
## IC11_IC12             -0.192876882                 14.349573
## IC12_IC17             -0.132893318                 60.262572
## IC12_IC20              0.000814278                -97.451328
## IC13_IC14              0.284849007               -246.656905
## IC14_IC16              0.405672732               -226.569219
## IC14_IC18             -0.013013514               -235.729572
## IC14_IC20             -0.172473911               -206.710456
## IC15_IC17              0.164531406                 37.129649
## IC17_IC18             -0.485237543                 53.078585
## IC18_IC19              0.045677851                -69.358310
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC12                  20.61705                 -0.4903790
## IC03_IC12                  45.89797                  2.5559765
## IC03_IC13                 -66.17664                  2.3885252
## IC03_IC18                 -50.19511                  0.2244515
## IC04_IC06                 242.86111                  0.1487822
## IC04_IC11                 211.33987                  0.5984408
## IC04_IC12                  36.81959                  2.6386990
## IC05_IC06                 172.59440                 -3.4567910
## IC05_IC19                 156.63629                  0.7698455
## IC07_IC13                 -41.66885                 -3.6712914
## IC08_IC11                 127.29728                  0.7814798
## IC08_IC20                 -93.42388                  1.6128900
## IC11_IC12                  33.95672                  0.5961633
## IC12_IC17                  79.86972                  1.8892709
## IC12_IC20                 -77.84418                 -0.5697457
## IC13_IC14                -227.04976                 -3.5571004
## IC14_IC16                -206.96207                 -2.4258463
## IC14_IC18                -216.12242                 -1.5180237
## IC14_IC20                -187.10331                  2.4606965
## IC15_IC17                  56.73680                  0.3329474
## IC17_IC18                  72.68573                  2.5402038
## IC18_IC19                 -49.75116                  1.9914692
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC12              0.6244114305              0.06059042
## IC03_IC12              0.0113429584             -0.36335796
## IC03_IC13              0.0178604678             -0.36514527
## IC03_IC18              0.8226386330             -0.03007772
## IC04_IC06              0.8818776982             -0.01843875
## IC04_IC11              0.5502333170             -0.08377147
## IC04_IC12              0.0089879596             -0.38545623
## IC05_IC06              0.0006696122              0.48303797
## IC05_IC19              0.4423136691             -0.10328839
## IC07_IC13              0.0003107532              0.51271271
## IC08_IC11              0.4354585223             -0.11556160
## IC08_IC20              0.1083691682             -0.23129206
## IC11_IC12              0.5517504974             -0.09921851
## IC12_IC17              0.0603244841             -0.26928952
## IC12_IC20              0.5694993271              0.08074945
## IC13_IC14              0.0004696920              0.49128413
## IC14_IC16              0.0161737397              0.32618819
## IC14_IC18              0.1306115703              0.20644564
## IC14_IC20              0.0147276617             -0.33189896
## IC15_IC17              0.7395280473             -0.04972028
## IC17_IC18              0.0118500978             -0.35626511
## IC18_IC19              0.0478109107             -0.27512176
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC12               -26.373181                -6.553351        0.7636743
## IC03_IC12                28.435303                48.255132       16.6928467
## IC03_IC13              -167.987023              -148.167194       11.6387970
## IC03_IC18               -27.342115                -7.522285        0.6298486
## IC04_IC06               219.555252               239.375081        0.6636211
## IC04_IC11               210.049655               229.869485        0.7585735
## IC04_IC12                28.932842                48.752671       16.2382806
## IC05_IC06               124.867452               144.687282       81.9293437
## IC05_IC19               125.539779               145.359608        0.9141969
## IC07_IC13              -117.659303               -97.839473      390.8687559
## IC08_IC11                74.236654                94.056483        0.7797743
## IC08_IC20               -90.644027               -70.824198        1.3733856
## IC11_IC12               -66.035966               -46.216136        0.7316592
## IC12_IC17                35.550528                55.370357        3.2894374
## IC12_IC20              -154.381457              -134.561628        0.7625460
## IC13_IC14              -235.786244              -215.966414      203.6196044
## IC14_IC16              -225.359769              -205.539939       12.0374592
## IC14_IC18              -206.117344              -186.297515        1.2864757
## IC14_IC20              -213.770772              -193.950943        9.5636043
## IC15_IC17                82.115061               101.934891        0.4468222
## IC17_IC18                34.642907                54.462737       13.6220191
## IC18_IC19                -8.440352                11.379477        1.5334740
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC12                 -1.6016220               0.111175445
## IC03_IC12                  1.6311594               0.104787333
## IC03_IC13                  3.0384977               0.002770001
## IC03_IC18                  1.3586932               0.176121067
## IC04_IC06                  0.7556826               0.450930176
## IC04_IC11                 -0.3284564               0.742988194
## IC04_IC12                  2.0471761               0.042246225
## IC05_IC06                  0.3439435               0.731332049
## IC05_IC19                  0.7223886               0.471090744
## IC07_IC13                 -1.6341733               0.104152376
## IC08_IC11                 -1.0622516               0.289692763
## IC08_IC20                  0.2430750               0.808252912
## IC11_IC12                  1.4249315               0.156088924
## IC12_IC17                  2.4057786               0.017257297
## IC12_IC20                 -0.4161891               0.677819095
## IC13_IC14                 -2.5232655               0.012584513
## IC14_IC16                 -2.5004840               0.013390659
## IC14_IC18                 -1.5732890               0.117590939
## IC14_IC20                  1.5217336               0.130013414
## IC15_IC17                 -1.5866893               0.114521033
## IC17_IC18                  2.7750025               0.006165397
## IC18_IC19                  1.4322503               0.153987109
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC12              0.24713844                -11.31508
## IC03_IC12             -0.27052920                 58.10547
## IC03_IC13             -0.50222278               -100.64322
## IC03_IC18             -0.22163203                -39.68638
## IC04_IC06             -0.13799203                203.69961
## IC04_IC11              0.04647632                177.96376
## IC04_IC12             -0.35563285                 29.53062
## IC05_IC06             -0.05147755                122.11045
## IC05_IC19             -0.11813608                112.95914
## IC07_IC13              0.24540684                -42.05848
## IC08_IC11              0.19185902                104.84243
## IC08_IC20             -0.04489083                -58.54158
## IC11_IC12             -0.24106889                 19.15637
## IC12_IC17             -0.40744891                 60.84566
## IC12_IC20              0.07097610                -88.48662
## IC13_IC14              0.42484206               -185.12389
## IC14_IC16              0.41686447               -212.78658
## IC14_IC18              0.29484340               -206.51828
## IC14_IC20             -0.27242468               -190.99132
## IC15_IC17              0.27735513                 29.37056
## IC17_IC18             -0.45581912                 58.39327
## IC18_IC19             -0.25228370                -87.23892
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC12                 7.392878                -0.3112332
## IC03_IC12                76.813438                 1.3715598
## IC03_IC13               -81.935259                 2.3499277
## IC03_IC18               -20.978417                 1.0375948
## IC04_IC06               222.407570                -0.9048711
## IC04_IC11               196.671727                 0.7716249
## IC04_IC12                48.238581                 0.3561788
## IC05_IC06               140.818415                -2.1415401
## IC05_IC19               131.667099                 1.3314022
## IC07_IC13               -23.350517                -2.7094089
## IC08_IC11               123.550392                -0.4828727
## IC08_IC20               -39.833614                 0.1755624
## IC11_IC12                37.864336                 2.4432320
## IC12_IC17                79.553620                 2.6815515
## IC12_IC20               -69.778657                -1.2062072
## IC13_IC14              -166.415931                -0.8444977
## IC14_IC16              -194.078621                -1.6690478
## IC14_IC18              -187.810316                -1.1928435
## IC14_IC20              -172.283360                 1.7499557
## IC15_IC17                48.078520                 0.2161937
## IC17_IC18                77.101236                 1.5816738
## IC18_IC19               -68.530956                 2.5214903
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC12              0.756021035            0.054571883
## IC03_IC12              0.172085561           -0.218804283
## IC03_IC13              0.019974826           -0.381536323
## IC03_IC18              0.300995495           -0.129538403
## IC04_IC06              0.366869175            0.181673077
## IC04_IC11              0.441453856           -0.156300658
## IC04_IC12              0.722167223           -0.066300183
## IC05_IC06              0.033716159            0.361054596
## IC05_IC19              0.184915728           -0.230584683
## IC07_IC13              0.007461023            0.470994388
## IC08_IC11              0.629833996            0.082135168
## IC08_IC20              0.860855660            0.025908126
## IC11_IC12              0.015623393           -0.387366952
## IC12_IC17              0.008082279           -0.417081872
## IC12_IC20              0.229485457            0.200067928
## IC13_IC14              0.399629134            0.184463431
## IC14_IC16              0.097027480            0.309502931
## IC14_IC18              0.234664128            0.199754146
## IC14_IC20              0.082007723           -0.269827359
## IC15_IC17              0.829107096            0.001739074
## IC17_IC18              0.115662520           -0.240649235
## IC18_IC19              0.012645736           -0.417388548
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC12              -23.483627               -4.775664       0.4923188
## IC03_IC12               14.336916               33.044879       1.7845001
## IC03_IC13             -122.920546             -104.212583       9.6795040
## IC03_IC18              -37.540287              -18.832325       1.1861277
## IC04_IC06              188.362149              207.070112       0.5358113
## IC04_IC11              208.410372              227.118335       0.7054349
## IC04_IC12               44.412064               63.120027       0.3722300
## IC05_IC06              115.376603              134.084566       1.4657777
## IC05_IC19              116.075315              134.783278       1.5631620
## IC07_IC13              -74.701619              -55.993656      19.4748090
## IC08_IC11               68.799714               87.507677       0.7331277
## IC08_IC20              -96.472495              -77.764532       0.7193256
## IC11_IC12              -52.391343              -33.683380      10.3433042
## IC12_IC17                9.536299               28.244262      23.7664845
## IC12_IC20             -125.750136             -107.042173       1.2533726
## IC13_IC14             -179.360228             -160.652265       0.5068066
## IC14_IC16             -167.612815             -148.904852       2.3981289
## IC14_IC18             -189.475459             -170.767496       1.3938615
## IC14_IC20             -269.871770             -251.163807       3.2059464
## IC15_IC17               67.299090               86.007052       0.3242601
## IC17_IC18               39.641534               58.349496       1.7467207
## IC18_IC19              -53.158157              -34.450194      12.0366791
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC12                         1.19563976                         0.2341945
## IC03_IC12                         0.57327322                         0.5675325
## IC03_IC13                        -0.94945435                         0.3442978
## IC03_IC18                        -0.68757172                         0.4930493
## IC04_IC06                        -0.45375807                         0.6508225
## IC04_IC11                         0.09066694                         0.9279084
## IC04_IC12                        -0.73436129                         0.4641611
## IC05_IC06                        -1.33609529                         0.1840463
## IC05_IC19                         0.17290018                         0.8630212
## IC07_IC13                        -0.51253646                         0.6092177
## IC08_IC11                         1.03016331                         0.3050052
## IC08_IC20                        -0.36295554                         0.7172766
## IC11_IC12                        -0.43290137                         0.6658635
## IC12_IC17                        -1.53781893                         0.1267256
## IC12_IC20                         0.42146339                         0.6741707
## IC13_IC14                         1.05375076                         0.2941142
## IC14_IC16                        -0.21458017                         0.8304589
## IC14_IC18                         1.61329638                         0.1093069
## IC14_IC20                        -0.40703430                         0.6847079
## IC15_IC17                         0.59555346                         0.5525951
## IC17_IC18                         0.19429799                         0.8462711
## IC18_IC19                        -1.45202147                         0.1491052
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC12                     -0.23746199                        -3.018572
## IC03_IC12                     -0.10968540                        47.802688
## IC03_IC13                      0.14689897                       -41.830474
## IC03_IC18                      0.10599110                       -39.024356
## IC04_IC06                      0.06920072                       171.330060
## IC04_IC11                     -0.04688927                       122.532051
## IC04_IC12                      0.12141200                        64.979549
## IC05_IC06                      0.24171590                       108.333373
## IC05_IC19                     -0.03266458                       144.266787
## IC07_IC13                      0.10889826                       -19.100543
## IC08_IC11                     -0.18535529                        86.526697
## IC08_IC20                      0.04925293                        -6.335306
## IC11_IC12                      0.05396153                         2.400852
## IC12_IC17                      0.27864192                        53.048196
## IC12_IC20                     -0.06860982                       -49.674870
## IC13_IC14                     -0.18481380                      -167.461254
## IC14_IC16                      0.01263080                      -130.472958
## IC14_IC18                     -0.28359193                      -121.742213
## IC14_IC20                      0.07682061                       -89.261416
## IC15_IC17                     -0.10667706                        33.886721
## IC17_IC18                     -0.02405240                        11.432187
## IC18_IC19                      0.25652223                       -11.191357
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC12                        13.854534                       -0.03816727
## IC03_IC12                        64.675794                        0.79428995
## IC03_IC13                       -24.957368                       -0.25384115
## IC03_IC18                       -22.151250                       -0.50040117
## IC04_IC06                       188.203166                        1.13495882
## IC04_IC11                       139.405158                       -0.76453816
## IC04_IC12                        81.852655                        1.66380603
## IC05_IC06                       125.206479                       -0.51261543
## IC05_IC19                       161.139894                       -0.57251121
## IC07_IC13                        -2.227437                       -0.09842180
## IC08_IC11                       103.399803                        1.13814737
## IC08_IC20                        10.537800                        1.51494716
## IC11_IC12                        19.273958                       -1.32796305
## IC12_IC17                        69.921302                       -0.72988394
## IC12_IC20                       -32.801764                        0.90860082
## IC13_IC14                      -150.588148                       -1.35806501
## IC14_IC16                      -113.599852                        0.13142358
## IC14_IC18                      -104.869107                       -0.20654360
## IC14_IC20                       -72.388310                        0.56155949
## IC15_IC17                        50.759827                        0.36209166
## IC17_IC18                        28.305294                        0.63832701
## IC18_IC19                         5.681749                       -0.42356551
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC12                       0.96961520                    0.007308656
## IC03_IC12                       0.42853204                   -0.137625535
## IC03_IC13                       0.80003535                    0.031516241
## IC03_IC18                       0.61767251                    0.097825153
## IC04_IC06                       0.25856422                   -0.180959924
## IC04_IC11                       0.44598750                    0.093129003
## IC04_IC12                       0.09865626                   -0.299153195
## IC05_IC06                       0.60912500                    0.109685062
## IC05_IC19                       0.56800400                    0.109273027
## IC07_IC13                       0.92175502                    0.017303175
## IC08_IC11                       0.25723569                   -0.183666063
## IC08_IC20                       0.13231041                   -0.228014478
## IC11_IC12                       0.18661010                    0.251668418
## IC12_IC17                       0.46682674                    0.112394078
## IC12_IC20                       0.36530935                   -0.101880519
## IC13_IC14                       0.17688930                    0.317164668
## IC14_IC16                       0.89565153                    0.005425268
## IC14_IC18                       0.83670237                    0.024557876
## IC14_IC20                       0.57542163                   -0.128090838
## IC15_IC17                       0.71789496                   -0.054704957
## IC17_IC18                       0.52442859                   -0.107790652
## IC18_IC19                       0.67261064                    0.072443272
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC12                       -2.899699                       14.212483
## IC03_IC12                       62.464131                       79.576312
## IC03_IC13                      -75.998411                      -58.886230
## IC03_IC18                      -19.857369                       -2.745187
## IC04_IC06                      174.940114                      192.052296
## IC04_IC11                      123.810415                      140.922597
## IC04_IC12                       45.860327                       62.972509
## IC05_IC06                      108.418974                      125.531156
## IC05_IC19                      140.306037                      157.418219
## IC07_IC13                      -24.289551                       -7.177369
## IC08_IC11                       80.221042                       97.333224
## IC08_IC20                      -15.558103                        1.554078
## IC11_IC12                        4.063025                       21.175207
## IC12_IC17                       45.276067                       62.388249
## IC12_IC20                      -56.337451                      -39.225269
## IC13_IC14                     -151.799981                     -134.687800
## IC14_IC16                     -140.822000                     -123.709819
## IC14_IC18                     -142.507275                     -125.395094
## IC14_IC20                     -115.040319                      -97.928137
## IC15_IC17                       51.495849                       68.608031
## IC17_IC18                       44.578449                       61.690631
## IC18_IC19                       40.437618                       57.549800
##           SCequalRRB_vs_SCoverRRB.repBF SCcorr_Disc.r SCcorr_Disc.t
## IC01_IC12                     0.4805766   0.021941964    0.42665054
## IC03_IC12                     0.9548092   0.122208052    0.97184339
## IC03_IC13                     0.6423274  -0.215653028   -2.15128320
## IC03_IC18                     0.7911175   0.002136498    0.32147605
## IC04_IC06                     0.7130299  -0.149270513   -1.72131113
## IC04_IC11                     0.7868123   0.053684900    0.96235535
## IC04_IC12                     0.6691546  -0.043082620   -0.32111128
## IC05_IC06                     0.6753121  -0.027760400   -0.54631033
## IC05_IC19                     0.7225772   0.089447081    1.03701535
## IC07_IC13                     0.6771370   0.028251426    0.25964948
## IC08_IC11                     1.3433948   0.116140332    1.60916326
## IC08_IC20                     0.9225405   0.061020958    0.48328981
## IC11_IC12                     1.3937420  -0.097223948   -0.95708854
## IC12_IC17                     0.7776360  -0.098155586   -1.12468193
## IC12_IC20                     1.0048041   0.079352256    0.45291808
## IC13_IC14                     0.4170092  -0.059875454   -0.39990554
## IC14_IC16                     0.6897642  -0.131419580   -1.33213089
## IC14_IC18                     0.3127558   0.082481691    1.08778758
## IC14_IC20                     0.6522730   0.109434809    0.72915968
## IC15_IC17                     0.7417239   0.039269703    0.07988827
## IC17_IC18                     0.8238009   0.127776852    1.07803010
## IC18_IC19                     0.5884339  -0.012813809   -0.55935459
##           SCcorr_Disc.pval SCcorr_Rep.r SCcorr_Rep.t SCcorr_Rep.pval
## IC01_IC12       0.67039832  0.003233085   0.15461485      0.87737433
## IC03_IC12       0.33308334 -0.033982102  -0.48475563      0.62869809
## IC03_IC13       0.03345810 -0.126963611  -1.12300938      0.26358581
## IC03_IC18       0.74840893 -0.153791633  -1.53703149      0.12681306
## IC04_IC06       0.08777141  0.161738712   1.67818048      0.09581035
## IC04_IC11       0.33780635 -0.018351087  -0.18924902      0.85020459
## IC04_IC12       0.74868464  0.008786831   0.44549366      0.65673363
## IC05_IC06       0.58586694  0.009072133  -0.13993955      0.88893298
## IC05_IC19       0.30181386 -0.036191267  -0.34401951      0.73140964
## IC07_IC13       0.79557911  0.056376570   0.26710893      0.78982547
## IC08_IC11       0.11020834  0.187959425   2.27255249      0.02476101
## IC08_IC20       0.62977061  0.200556463   2.05566773      0.04189683
## IC11_IC12       0.34044681 -0.124975082  -1.24159464      0.21671088
## IC12_IC17       0.26296847 -0.203831211  -2.31675788      0.02214415
## IC12_IC20       0.65142557  0.219000361   2.19566290      0.02996203
## IC13_IC14       0.68993704 -0.016340357   0.07893432      0.93721109
## IC14_IC16       0.18534161  0.065191885   0.98790761      0.32510632
## IC14_IC18       0.27886889  0.069255232   0.99406973      0.32210933
## IC14_IC20       0.46732477 -0.034469408  -0.67167717      0.50302870
## IC15_IC17       0.93645920 -0.068463813  -1.08495884      0.28002795
## IC17_IC18       0.28318260 -0.097275106  -1.20345834      0.23107323
## IC18_IC19       0.57696224 -0.028593224  -0.52451062      0.60085236
##           SCcorr.repBF RRBcorr_Disc.r RRBcorr_Disc.t RRBcorr_Disc.pval
## IC01_IC12    0.6954284    0.022253885     0.41920559        0.67581534
## IC03_IC12    0.4613062   -0.052197602    -0.61712249        0.53832312
## IC03_IC13    0.9918510   -0.100984042    -0.98383037        0.32717839
## IC03_IC18    0.9684752    0.087762907     1.07295336        0.28544500
## IC04_IC06    0.1627004   -0.088447737    -1.13301352        0.25946725
## IC04_IC11    0.5096450    0.001223994     0.85033233        0.39683381
## IC04_IC12    0.6674887   -0.018878034    -0.10172859        0.91914190
## IC05_IC06    0.6776660    0.129883126     1.02441277        0.30770101
## IC05_IC19    0.4593306    0.111258537     1.19479533        0.23452312
## IC07_IC13    0.7256737    0.006504303     0.17706303        0.85975739
## IC08_IC11    8.0866009    0.140225485     1.70980028        0.08988736
## IC08_IC20    3.1215669    0.004305451    -0.25344479        0.80035822
## IC11_IC12    1.4854833   -0.065262479    -0.60958908        0.54328656
## IC12_IC17    7.0481993    0.268013929     2.12742673        0.03543136
## IC12_IC20    3.6324648    0.007883081    -0.64080908        0.52286861
## IC13_IC14    0.6630706   -0.033549747    -0.07222455        0.94254345
## IC14_IC16    0.2967348   -0.020705032    -0.12606605        0.89989062
## IC14_IC18    1.1442546    0.052195690     0.95395157        0.34202584
## IC14_IC20    0.5359185    0.059448943     0.13091575        0.89606118
## IC15_IC17    0.9042265    0.059630717     0.24894077        0.80383217
## IC17_IC18    0.3945256    0.066930613     0.21592829        0.82941025
## IC18_IC19    0.8030510    0.078735341     0.11598313        0.90785978
##           RRBcorr_Rep.r RRBcorr_Rep.t RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12  -0.031570729    0.43793056      0.662192433     0.7712664
## IC03_IC12  -0.268313828   -2.64576491      0.009197724     8.0245238
## IC03_IC13  -0.105545423   -0.97743952      0.330239493     1.1285575
## IC03_IC18  -0.191165725   -1.56211423      0.120789313     0.4190989
## IC04_IC06   0.131186457    1.17183634      0.243491459     0.3703527
## IC04_IC11  -0.006080479    0.69246450      0.489930085     0.8848918
## IC04_IC12  -0.132022831   -1.28323728      0.201782973     1.1339116
## IC05_IC06   0.008273737    0.01175331      0.990641170     0.5392303
## IC05_IC19   0.020045801    0.12026905      0.904463122     0.5252455
## IC07_IC13   0.080865498    0.37982124      0.704722665     0.7459577
## IC08_IC11   0.234578549    2.30655588      0.022725465     8.9081085
## IC08_IC20   0.135383657    1.14996703      0.252352808     0.8330652
## IC11_IC12  -0.029488716   -0.56718002      0.571609043     0.8222671
## IC12_IC17  -0.111746204   -0.72156532      0.471909306     0.1178531
## IC12_IC20   0.291944708    2.56164090      0.011605625     1.4242822
## IC13_IC14   0.240763309    2.83555055      0.005337477     4.5492368
## IC14_IC16   0.131510531    1.53218304      0.128004284     1.1475421
## IC14_IC18   0.206087538    2.46637852      0.015003756     8.0998250
## IC14_IC20  -0.196980403   -2.08910559      0.038726525     1.8179576
## IC15_IC17   0.181375760    1.51968554      0.131115427     1.4923754
## IC17_IC18  -0.131431759   -1.21259283      0.227572306     0.8816555
## IC18_IC19  -0.085669113   -1.45846024      0.147221837     1.1002032
##           SumSCRRB_Disc.r SumSCRRB_Disc.t SumSCRRB_Disc.pval SumSCRRB_Rep.r
## IC01_IC12      0.02901956       0.5310098         0.59639342    -0.01216495
## IC03_IC12      0.05598002       0.2238562         0.82324983    -0.14992126
## IC03_IC13     -0.21386179      -1.9781943         0.05019802    -0.14306998
## IC03_IC18      0.05422217       0.8827275         0.37914842    -0.20242756
## IC04_IC06     -0.15934662      -1.8436439         0.06770220     0.18064959
## IC04_IC11      0.03906505       1.0959706         0.27528630    -0.01646374
## IC04_IC12     -0.04208466      -0.2700538         0.78758276    -0.05450550
## IC05_IC06      0.05825782       0.2156622         0.82961723     0.01057013
## IC05_IC19      0.13048683       1.3482337         0.18012260    -0.01768556
## IC07_IC13      0.02407416       0.2742754         0.78434455     0.07926642
## IC08_IC11      0.16676443       2.0117014         0.04649155     0.24768499
## IC08_IC20      0.04615055       0.1748611         0.86148346     0.21130671
## IC11_IC12     -0.10848355      -0.9888179         0.32474187    -0.10658917
## IC12_IC17      0.09204190       0.4721155         0.63770172    -0.20289733
## IC12_IC20      0.06138142      -0.0445753         0.96451992     0.29703865
## IC13_IC14     -0.06286582      -0.3133240         0.75457845     0.09966974
## IC14_IC16     -0.10620909      -0.8854186         0.37770170     0.10922603
## IC14_IC18      0.09015854       1.3279541         0.18671367     0.14686474
## IC14_IC20      0.11370131       0.5391124         0.59080811    -0.11684976
## IC15_IC17      0.06380923       0.2176714         0.82805483     0.03307955
## IC17_IC18      0.13122882       0.8776867         0.38186762    -0.13299202
## IC18_IC19      0.03812920      -0.3085780         0.75817758    -0.06083131
##           SumSCRRB_Rep.t SumSCRRB_Rep.pval SumSCRRB.repBF   zds_Disc.r
## IC01_IC12     0.32693046       0.744267295      0.7303170  0.002657793
## IC03_IC12    -1.61197695       0.109489038      1.1153558  0.136402752
## IC03_IC13    -1.27302692       0.205371003      1.3712965 -0.107756293
## IC03_IC18    -1.82520174       0.070358059      0.5953742 -0.058854280
## IC04_IC06     1.76067758       0.080738597      0.1326764 -0.061618574
## IC04_IC11     0.09844675       0.921735250      0.5455604  0.043233647
## IC04_IC12    -0.35513921       0.723083828      0.7448749 -0.022345781
## IC05_IC06    -0.09186411       0.926953069      0.6863482 -0.112569212
## IC05_IC19    -0.18477365       0.853706029      0.3926020 -0.003416208
## IC07_IC13     0.36927339       0.712548753      0.7484291  0.018705713
## IC08_IC11     2.70863823       0.007703423     22.7219001 -0.001563469
## IC08_IC20     2.02789026       0.044697063      2.3252346  0.047133160
## IC11_IC12    -1.12832858       0.261342137      1.3193817 -0.034806562
## IC12_IC17    -1.93457578       0.055301730      1.0775135 -0.266888039
## IC12_IC20     2.85725380       0.005006840      4.8818904  0.059725308
## IC13_IC14     1.46765418       0.144709887      0.9384866 -0.026012431
## IC14_IC16     1.43006656       0.155192719      0.5141249 -0.093700405
## IC14_IC18     1.94080748       0.054532693      4.1581081  0.031713651
## IC14_IC20    -1.47966288       0.141479102      0.7598084  0.048866197
## IC15_IC17    -0.03661856       0.970847557      0.6895181 -0.008920903
## IC17_IC18    -1.41513365       0.159515783      0.5153622  0.058791490
## IC18_IC19    -1.12958502       0.260814123      1.1256414 -0.064881831
##            zds_Disc.t zds_Disc.pval    zds_Rep.r    zds_Rep.t zds_Rep.pval
## IC01_IC12  0.02685358   0.978621128  0.023309478 -0.158594291   0.87424447
## IC03_IC12  1.43101724   0.155024347  0.136734419  1.453557345   0.14857512
## IC03_IC13 -0.99853453   0.320029498 -0.062880426 -0.430408517   0.66763968
## IC03_IC18 -0.56954469   0.570051184 -0.035681414 -0.437707139   0.66235397
## IC04_IC06 -0.59680280   0.551763355  0.082200387  0.912711949   0.36315105
## IC04_IC11  0.15616824   0.876162830 -0.014899531 -0.587190628   0.55813481
## IC04_IC12 -0.19469984   0.845957187  0.092781160  1.438981648   0.15265506
## IC05_IC06 -1.30640519   0.193913203  0.004027948 -0.149721114   0.88122595
## IC05_IC19 -0.11501962   0.908621815 -0.049681652 -0.445095420   0.65702061
## IC07_IC13  0.08289200   0.934075525  0.006338976 -0.005453223   0.99565767
## IC08_IC11 -0.04063034   0.967658120  0.042788914  0.643662229   0.52097367
## IC08_IC20  0.60851801   0.543994106  0.119371439  1.294230664   0.19797173
## IC11_IC12 -0.33824990   0.735765943 -0.109081175 -0.878497723   0.38135921
## IC12_IC17 -2.80788842   0.005822396 -0.137767387 -1.880084783   0.06242402
## IC12_IC20  0.79435046   0.428559577  0.037417649  0.646003575   0.51946125
## IC13_IC14 -0.28190050   0.778505391 -0.170165849 -1.763193184   0.08031152
## IC14_IC16 -1.08983062   0.277971435 -0.016876321 -0.110565618   0.91213814
## IC14_IC18  0.22174035   0.824892929 -0.060486527 -0.624828062   0.53322281
## IC14_IC20  0.54296251   0.588162711  0.090168120  0.851331964   0.39621295
## IC15_IC17 -0.11415196   0.909308101 -0.185111864 -2.076675164   0.03988013
## IC17_IC18  0.73741185   0.462311380 -0.015996310 -0.353999369   0.72393577
## IC18_IC19 -0.55720363   0.578426142  0.025111679  0.423527791   0.67263808
##           zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Disc.t
## IC01_IC12 0.7031229                 0.08453430                0.582817696
## IC03_IC12 2.0059578                -0.01155316               -0.077144165
## IC03_IC13 0.7063224                -0.22792682               -1.632784157
## IC03_IC18 0.7661179                -0.14515754               -0.756493858
## IC04_IC06 0.6019329                -0.23983031               -2.208760572
## IC04_IC11 0.7263378                 0.07979150                1.019421240
## IC04_IC12 1.0215623                 0.00970710               -0.067932710
## IC05_IC06 0.5024849                 0.08476974                0.160525539
## IC05_IC19 0.7523901                 0.26829860                1.747233429
## IC07_IC13 0.6984898                -0.06478857               -0.314035300
## IC08_IC11 0.7679910                 0.20314909                2.070569334
## IC08_IC20 1.4464655                 0.10572511                0.517053629
## IC11_IC12 0.9605286                -0.16341962               -1.173901714
## IC12_IC17 3.1878337                 0.09933078                0.779981721
## IC12_IC20 0.8578478                 0.17634246                0.786818610
## IC13_IC14 1.9240908                -0.21411146               -1.145432836
## IC14_IC16 0.5499091                -0.08390345               -1.081104852
## IC14_IC18 0.7130210                 0.08960937                1.124692464
## IC14_IC20 0.9848591                 0.08161356                0.223687292
## IC15_IC17 2.3174567                 0.04856125                0.211003814
## IC17_IC18 0.5522379                 0.09205547               -0.008428301
## IC18_IC19 0.6018958                 0.09847882                0.086778309
##           SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12                    0.56188888              -0.051429808
## IC03_IC12                    0.93872894              -0.252101805
## IC03_IC13                    0.10700501              -0.070945668
## IC03_IC18                    0.45189173              -0.346254372
## IC04_IC06                    0.03046698               0.192596559
## IC04_IC11                    0.31151345              -0.068780809
## IC04_IC12                    0.94603287              -0.216566499
## IC05_IC06                    0.87292946              -0.044465200
## IC05_IC19                    0.08498255              -0.002427627
## IC07_IC13                    0.75442822               0.133901003
## IC08_IC11                    0.04208890               0.237543592
## IC08_IC20                    0.60674866               0.226051761
## IC11_IC12                    0.24441293              -0.217659882
## IC12_IC17                    0.43803105              -0.232918057
## IC12_IC20                    0.43404386               0.385294634
## IC13_IC14                    0.25593092               0.330555609
## IC14_IC16                    0.28336103               0.159239090
## IC14_IC18                    0.26456122               0.244718725
## IC14_IC20                    0.82365218              -0.220639840
## IC15_IC17                    0.83349736               0.149623778
## IC17_IC18                    0.99329924              -0.137009332
## IC18_IC19                    0.93109548              -0.128455963
##           SumSCRRB_SCequalRRB_Rep.t SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12               -0.03152289                  0.974935179
## IC03_IC12               -2.13849712                  0.035689420
## IC03_IC13               -0.57029448                  0.570160190
## IC03_IC18               -2.85673153                  0.005517810
## IC04_IC06                1.39921881                  0.165815584
## IC04_IC11               -0.20727979                  0.836345746
## IC04_IC12               -1.61250712                  0.110996817
## IC05_IC06               -0.36784294                  0.714012828
## IC05_IC19               -0.04605968                  0.963383500
## IC07_IC13                1.12000533                  0.266238495
## IC08_IC11                1.82595541                  0.071784762
## IC08_IC20                1.60816070                  0.111946658
## IC11_IC12               -1.62430129                  0.108452055
## IC12_IC17               -1.49044391                  0.140246358
## IC12_IC20                2.97929133                  0.003877243
## IC13_IC14                2.92181083                  0.004580470
## IC14_IC16                1.21745530                  0.227197717
## IC14_IC18                2.56785351                  0.012195743
## IC14_IC20               -1.71566351                  0.090297943
## IC15_IC17                0.99306528                  0.323829438
## IC17_IC18               -0.83574009                  0.405920958
## IC18_IC19               -1.06094407                  0.292075276
##           SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Disc.t
## IC01_IC12                0.63757610          -0.304764964           -2.62297571
## IC03_IC12                2.38392316           0.053429273            0.42918135
## IC03_IC13                0.61429155           0.056651156            0.62283881
## IC03_IC18               13.08618251          -0.101232305           -0.74846253
## IC04_IC06                0.07384865          -0.108473930           -0.64194979
## IC04_IC11                0.48880187          -0.082045454           -1.10763632
## IC04_IC12                1.42618135           0.043130364            0.48226160
## IC05_IC06                0.69969425          -0.062024451           -0.31237974
## IC05_IC19                0.30896018           0.059003703            0.35474036
## IC07_IC13                0.78795137          -0.136142503           -1.27015592
## IC08_IC11                3.58418088          -0.034850679           -0.78564575
## IC08_IC20                1.90443486           0.181212224            2.07937145
## IC11_IC12                2.48406719           0.084597673            0.62105121
## IC12_IC17                0.59018732          -0.150194639           -1.03967465
## IC12_IC20               16.66623696          -0.042696936           -0.03270187
## IC13_IC14                0.79291236          -0.315130289           -2.94806341
## IC14_IC16                0.39291048          -0.158107047           -1.03140685
## IC14_IC18               10.85032508          -0.255930003           -2.34168223
## IC14_IC20                1.20057044           0.080616740            0.72507206
## IC15_IC17                0.98902056          -0.019421667           -0.24120391
## IC17_IC18                0.83935203           0.065140888            1.13799473
## IC18_IC19                0.88977031          -0.000418521            0.34291476
##           zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.r zds_SCequalRRB_Rep.t
## IC01_IC12              0.010689632          0.007186694          -0.15359940
## IC03_IC12              0.669109307          0.238823062           2.03973254
## IC03_IC13              0.535414365         -0.048901495          -0.28393741
## IC03_IC18              0.456688706         -0.034137618          -0.29397109
## IC04_IC06              0.523002532          0.009091615           0.09055235
## IC04_IC11              0.271810462          0.029230550           0.03236764
## IC04_IC12              0.631124715          0.019854650           0.33948477
## IC05_IC06              0.755680619          0.046155169           0.29663956
## IC05_IC19              0.723850643          0.024552826           0.26943251
## IC07_IC13              0.208234888          0.059794448           0.35273846
## IC08_IC11              0.434726334          0.045497331           0.59301281
## IC08_IC20              0.041248059         -0.043494587          -0.34197000
## IC11_IC12              0.536583057          0.061218318           0.74893694
## IC12_IC17              0.302068917         -0.235150588          -2.21792356
## IC12_IC20              0.974005398         -0.139094881          -0.93198121
## IC13_IC14              0.004342648          0.004083003           0.02722981
## IC14_IC16              0.305900575          0.002437298           0.01237293
## IC14_IC18              0.022048419         -0.032115490          -0.36308560
## IC14_IC20              0.470824746          0.106186082           0.80047945
## IC15_IC17              0.810102206         -0.321794127          -2.98895302
## IC17_IC18              0.259002731         -0.150301757          -1.51190713
## IC18_IC19              0.732690251          0.136700449           1.26720853
##           zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF zds_SCoverRRB_Disc.r
## IC01_IC12             0.878332731           0.15178244           0.12514764
## IC03_IC12             0.044852452           2.92923690           0.31483942
## IC03_IC13             0.777230467           0.59291183          -0.35132901
## IC03_IC18             0.769581356           0.69309880           0.15002844
## IC04_IC06             0.928086526           0.61291571           0.16360124
## IC04_IC11             0.974263725           0.50296076           0.18858525
## IC04_IC12             0.735180922           0.73826492          -0.09421679
## IC05_IC06             0.767550870           0.66639479          -0.11190641
## IC05_IC19             0.788327041           0.72427852          -0.22911017
## IC07_IC13             0.725260931           0.38410749           0.49912811
## IC08_IC11             0.554933175           0.51876484          -0.27380487
## IC08_IC20             0.733317305           0.16871633           0.07577193
## IC11_IC12             0.456206960           0.92429365          -0.15366924
## IC12_IC17             0.029547179           5.72240258          -0.48501988
## IC12_IC20             0.354297525           0.88759179           0.27712226
## IC13_IC14             0.978347811           0.07565999           0.01863420
## IC14_IC16             0.990160505           0.52988358          -0.32092795
## IC14_IC18             0.717548856           0.27604698          -0.07593056
## IC14_IC20             0.425928307           0.96437838           0.22447372
## IC15_IC17             0.003769356           8.76381163          -0.22142959
## IC17_IC18             0.134704708           0.38382072           0.17504276
## IC18_IC19             0.208950037           1.27274777           0.16724776
##           zds_SCoverRRB_Disc.t zds_SCoverRRB_Disc.pval zds_SCoverRRB_Rep.r
## IC01_IC12            0.7490161             0.457925466         -0.01383797
## IC03_IC12            2.1403273             0.038041964         -0.09835797
## IC03_IC13           -2.1436523             0.037758465         -0.05521216
## IC03_IC18            0.8174150             0.418197561          0.07306033
## IC04_IC06            0.7846089             0.436984271         -0.02879048
## IC04_IC11            1.5147114             0.137162322          0.03255199
## IC04_IC12           -0.1475780             0.883365684         -0.02818725
## IC05_IC06           -0.6188697             0.539267142          0.05835806
## IC05_IC19           -1.1872426             0.241649389         -0.04303200
## IC07_IC13            3.3828977             0.001538571         -0.04158595
## IC08_IC11           -1.6807905             0.100054802         -0.22139803
## IC08_IC20            0.1820616             0.856389971          0.10609126
## IC11_IC12           -0.6716776             0.505381795         -0.06426293
## IC12_IC17           -3.2219896             0.002428651         -0.07174130
## IC12_IC20            1.4104955             0.165586954          0.17403741
## IC13_IC14           -0.1326373             0.895098975         -0.26895060
## IC14_IC16           -1.9008935             0.064032091         -0.08336991
## IC14_IC18           -0.3440332             0.732498079         -0.16472460
## IC14_IC20            1.4590414             0.151820584         -0.06365687
## IC15_IC17           -1.8183807             0.075975895         -0.44367638
## IC17_IC18            1.0272717             0.310036782         -0.08765311
## IC18_IC19            0.9430599             0.350918942          0.04113726
##           zds_SCoverRRB_Rep.t zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF
## IC01_IC12          -0.1476998            0.883286514          0.57697056
## IC03_IC12          -0.6601649            0.512752442          0.12125287
## IC03_IC13          -0.4324940            0.667595070          0.36356118
## IC03_IC18           0.4123109            0.682208585          0.73059618
## IC04_IC06          -0.1428225            0.887113396          0.56890841
## IC04_IC11           0.1844043            0.854583787          0.45261707
## IC04_IC12          -0.1633849            0.870999053          0.70866468
## IC05_IC06           0.4374403            0.664033284          0.58228054
## IC05_IC19          -0.2953069            0.769214412          0.59585482
## IC07_IC13          -0.1405522            0.888895622          0.03157664
## IC08_IC11          -1.5481815            0.129081609          2.28966778
## IC08_IC20           0.6816124            0.499225579          0.83225718
## IC11_IC12          -0.3733463            0.710768595          0.73294943
## IC12_IC17          -0.5284882            0.599942240          0.12966061
## IC12_IC20           1.1605112            0.252394877          1.34700523
## IC13_IC14          -1.7524818            0.086989107          1.69900539
## IC14_IC16          -0.5476998            0.586797138          0.50748612
## IC14_IC18          -1.0329461            0.307540628          1.06590557
## IC14_IC20          -0.3057028            0.761341021          0.33330483
## IC15_IC17          -3.0464006            0.003992099         44.91523696
## IC17_IC18          -0.5159395            0.608602301          0.43959511
## IC18_IC19           0.2933284            0.770715661          0.65484908
##           VinelandABC_Disc.r VinelandABC_Disc.t VinelandABC_Disc.pval
## IC01_IC12        0.117928538         1.28839305            0.20008779
## IC03_IC12       -0.072641630        -0.90259428            0.36854945
## IC03_IC13       -0.134772608        -1.21171075            0.22800372
## IC03_IC18       -0.061046059        -0.40567733            0.68570212
## IC04_IC06        0.071190982         0.74733356            0.45632415
## IC04_IC11       -0.103254277        -1.19071242            0.23611656
## IC04_IC12        0.002441248         0.09422101            0.92509074
## IC05_IC06        0.057668657         0.34139602            0.73340254
## IC05_IC19       -0.143747879        -1.51313280            0.13287524
## IC07_IC13        0.073694498         0.64281122            0.52157295
## IC08_IC11       -0.041406876        -0.28936272            0.77280316
## IC08_IC20       -0.012128605        -0.05912888            0.95294781
## IC11_IC12        0.056106901         0.59532805            0.55274527
## IC12_IC17        0.016795507         0.21690377            0.82865168
## IC12_IC20        0.027880754         0.22720128            0.82065386
## IC13_IC14        0.025951362         0.40944604            0.68294229
## IC14_IC16        0.202370386         2.00841180            0.04684481
## IC14_IC18        0.118454122         1.22434700            0.22321958
## IC14_IC20       -0.182685897        -2.06849525            0.04074162
## IC15_IC17       -0.025714298        -0.44972704            0.65371858
## IC17_IC18       -0.162183284        -1.71739838            0.08848605
## IC18_IC19       -0.095770335        -1.10850604            0.26986009
##           VinelandABC_Rep.r VinelandABC_Rep.t VinelandABC_Rep.pval
## IC01_IC12       0.177064715        1.84236559          0.067791395
## IC03_IC12       0.007354335        0.03012218          0.976017651
## IC03_IC13       0.075485086        0.99794471          0.320234088
## IC03_IC18      -0.002086721       -0.06239543          0.950347592
## IC04_IC06      -0.250785296       -2.79756054          0.005964628
## IC04_IC11      -0.112075279       -1.25531477          0.211706170
## IC04_IC12      -0.131798765       -1.22488546          0.222921592
## IC05_IC06       0.039613563        0.31034038          0.756818969
## IC05_IC19      -0.211936396       -2.24557972          0.026487633
## IC07_IC13      -0.046360656       -0.64138661          0.522445833
## IC08_IC11       0.007092690        0.30054629          0.764259781
## IC08_IC20      -0.214624658       -2.41438023          0.017210910
## IC11_IC12       0.056210538        0.70922509          0.479505554
## IC12_IC17       0.053290790        0.37911699          0.705244219
## IC12_IC20      -0.077436310       -0.80379023          0.423044058
## IC13_IC14       0.026756794        0.34334322          0.731917061
## IC14_IC16      -0.075385606       -0.66711643          0.505927395
## IC14_IC18       0.062231634        0.77226639          0.441414333
## IC14_IC20      -0.218013422       -2.55029050          0.011970586
## IC15_IC17      -0.097459593       -1.20257392          0.231414244
## IC17_IC18      -0.065270821       -0.87488330          0.383315323
## IC18_IC19       0.141866569        1.51854409          0.131402514
##           VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12         3.5207973                  0.1067004255
## IC03_IC12         0.5607317                 -0.1006967711
## IC03_IC13         0.3406041                 -0.2073287286
## IC03_IC18         0.6802684                  0.0144610042
## IC04_IC06         1.4756638                 -0.0568329390
## IC04_IC11         1.5367022                 -0.1012964197
## IC04_IC12         0.9670556                 -0.1555691202
## IC05_IC06         0.7353533                  0.1376289705
## IC05_IC19         7.4441019                 -0.2372982915
## IC07_IC13         0.5695473                  0.1002723097
## IC08_IC11         0.6711232                 -0.3478406641
## IC08_IC20         3.2002400                 -0.1796873079
## IC11_IC12         0.8989774                 -0.1793628342
## IC12_IC17         0.7492798                 -0.1076469471
## IC12_IC20         0.7447499                 -0.0006189235
## IC13_IC14         0.7423939                  0.0191741885
## IC14_IC16         0.1446152                  0.2976287607
## IC14_IC18         0.8937073                  0.1247968842
## IC14_IC20        16.1666432                 -0.2929031944
## IC15_IC17         1.2606998                  0.0579360643
## IC17_IC18         0.8498884                 -0.0794348038
## IC18_IC19         0.3986396                 -0.1249865158
##           VinelandABC_SCequalRRB_Disc.t VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12                     0.7193601                      0.474313869
## IC03_IC12                    -0.7420718                      0.460526571
## IC03_IC13                    -1.3714328                      0.174620368
## IC03_IC18                     0.4702458                      0.639641254
## IC04_IC06                    -0.4998388                      0.618756230
## IC04_IC11                    -0.8604551                      0.392475830
## IC04_IC12                    -1.0730762                      0.286922336
## IC05_IC06                     0.7972287                      0.428014151
## IC05_IC19                    -1.9426155                      0.056085994
## IC07_IC13                     0.5657419                      0.573378368
## IC08_IC11                    -2.7921213                      0.006745789
## IC08_IC20                    -1.2031270                      0.232980570
## IC11_IC12                    -1.3611376                      0.177836017
## IC12_IC17                    -0.5094735                      0.612022813
## IC12_IC20                    -0.1981194                      0.843525917
## IC13_IC14                     0.2725603                      0.785993679
## IC14_IC16                     2.2322891                      0.028797530
## IC14_IC18                     0.9581084                      0.341305658
## IC14_IC20                    -2.3405824                      0.022108601
## IC15_IC17                     0.1664686                      0.868268122
## IC17_IC18                    -0.5888137                      0.557881568
## IC18_IC19                    -1.1508268                      0.253719517
##           VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Rep.t
## IC01_IC12                 -0.028225551                   -0.3172077
## IC03_IC12                 -0.062394517                   -0.6723051
## IC03_IC13                  0.028136233                    0.4248429
## IC03_IC18                  0.081908225                    0.5913030
## IC04_IC06                 -0.378369363                   -3.3696812
## IC04_IC11                  0.203348437                    1.5505413
## IC04_IC12                 -0.129102367                   -0.7942286
## IC05_IC06                  0.196632228                    1.4833985
## IC05_IC19                 -0.343753200                   -2.9453116
## IC07_IC13                 -0.129714056                   -1.1986963
## IC08_IC11                  0.109095820                    1.1808637
## IC08_IC20                 -0.380912406                   -3.4161414
## IC11_IC12                  0.053705671                    0.7809750
## IC12_IC17                  0.100371752                    0.7427915
## IC12_IC20                 -0.003099587                    0.0138864
## IC13_IC14                  0.043940453                    0.5954126
## IC14_IC16                 -0.045257523                   -0.3420221
## IC14_IC18                  0.008059356                    0.2112618
## IC14_IC20                 -0.294362907                   -2.6411860
## IC15_IC17                 -0.106195933                   -1.2274268
## IC17_IC18                 -0.093917095                   -0.9365967
## IC18_IC19                  0.157557469                    1.3185571
##           VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12                     0.751955781                   0.56125646
## IC03_IC12                     0.503428338                   0.87585412
## IC03_IC13                     0.672151604                   0.33929349
## IC03_IC18                     0.556072065                   0.83252710
## IC04_IC06                     0.001184757                  23.41869444
## IC04_IC11                     0.125166136                   0.54954983
## IC04_IC12                     0.429535418                   0.93927505
## IC05_IC06                     0.142103918                   1.87510553
## IC05_IC19                     0.004279808                  38.01376662
## IC07_IC13                     0.234370431                   0.66420106
## IC08_IC11                     0.241339263                   0.02836349
## IC08_IC20                     0.001022701                  61.02756904
## IC11_IC12                     0.437243122                   0.30097777
## IC12_IC17                     0.459897734                   0.62415799
## IC12_IC20                     0.988957004                   0.69163219
## IC13_IC14                     0.553336609                   0.81526491
## IC14_IC16                     0.733278256                   0.14004165
## IC14_IC18                     0.833248475                   0.61992832
## IC14_IC20                     0.010024113                  20.86007442
## IC15_IC17                     0.223450591                   0.92236744
## IC17_IC18                     0.351932765                   1.05624652
## IC18_IC19                     0.191276991                   0.36574951
##           VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Disc.t
## IC01_IC12                   0.17450773                    1.1500832
## IC03_IC12                  -0.05829850                   -0.5443349
## IC03_IC13                  -0.05493026                   -0.2813511
## IC03_IC18                  -0.13645941                   -0.7687423
## IC04_IC06                   0.23460265                    1.5294011
## IC04_IC11                  -0.10925131                   -0.8061005
## IC04_IC12                   0.15073623                    0.8948775
## IC05_IC06                  -0.01252665                   -0.1058172
## IC05_IC19                  -0.05625207                   -0.3141004
## IC07_IC13                   0.06692883                    0.2902638
## IC08_IC11                   0.24185171                    1.7131367
## IC08_IC20                   0.12353340                    0.7455110
## IC11_IC12                   0.30720503                    2.0424524
## IC12_IC17                   0.14696804                    0.9127734
## IC12_IC20                   0.06309952                    0.4465113
## IC13_IC14                   0.01091215                    0.2148629
## IC14_IC16                   0.11380876                    0.8067673
## IC14_IC18                   0.07760485                    0.5524788
## IC14_IC20                  -0.06185320                   -0.5298528
## IC15_IC17                  -0.11811210                   -0.6428318
## IC17_IC18                  -0.27698340                   -1.9000671
## IC18_IC19                  -0.04723721                   -0.1989041
##           VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.r
## IC01_IC12                      0.25646580                 0.536418218
## IC03_IC12                      0.58902280                 0.165714100
## IC03_IC13                      0.77979061                 0.076907406
## IC03_IC18                      0.44624818                -0.176766966
## IC04_IC06                      0.13348894                -0.026735180
## IC04_IC11                      0.42462069                -0.488919436
## IC04_IC12                      0.37583455                -0.163987447
## IC05_IC06                      0.91621952                -0.131175026
## IC05_IC19                      0.75496364                -0.023597413
## IC07_IC13                      0.77300968                 0.055654422
## IC08_IC11                      0.09388985                -0.158762892
## IC08_IC20                      0.46001880                 0.067336298
## IC11_IC12                      0.04726877                 0.005041906
## IC12_IC17                      0.36645122                -0.096416357
## IC12_IC20                      0.65746863                -0.193767438
## IC13_IC14                      0.83088998                -0.012687804
## IC14_IC16                      0.42424047                -0.107986615
## IC14_IC18                      0.58348099                 0.260483377
## IC14_IC20                      0.59893938                -0.046077450
## IC15_IC17                      0.52374645                -0.077664969
## IC17_IC18                      0.06414327                -0.042239434
## IC18_IC19                      0.84327532                 0.126906127
##           VinelandABC_SCoverRRB_Rep.t VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12                  3.86729079                   0.0003763654
## IC03_IC12                  1.08004316                   0.2862861949
## IC03_IC13                  0.62371849                   0.5361854701
## IC03_IC18                 -1.02013170                   0.3135064402
## IC04_IC06                 -0.24538734                   0.8073516501
## IC04_IC11                 -3.57126741                   0.0009068565
## IC04_IC12                 -1.02811846                   0.3097789492
## IC05_IC06                 -0.85571450                   0.3970113293
## IC05_IC19                  0.01214623                   0.9903664593
## IC07_IC13                  0.28468705                   0.7772829569
## IC08_IC11                 -0.93550241                   0.3548774338
## IC08_IC20                  0.32228361                   0.7488366371
## IC11_IC12                  0.11551878                   0.9085844864
## IC12_IC17                 -0.49286836                   0.6246726416
## IC12_IC20                 -1.40224746                   0.1681895928
## IC13_IC14                 -0.08894350                   0.9295495843
## IC14_IC16                 -0.62831023                   0.5332027590
## IC14_IC18                  1.71612132                   0.0935084324
## IC14_IC20                 -0.34506671                   0.7317663965
## IC15_IC17                 -0.48841989                   0.6277929619
## IC17_IC18                 -0.41620867                   0.6793765600
## IC18_IC19                  0.81873578                   0.4175585985
##           VinelandABC_SCoverRRB.repBF
## IC01_IC12                 152.6207509
## IC03_IC12                   0.6511909
## IC03_IC13                   0.6945975
## IC03_IC18                   1.1612551
## IC04_IC06                   0.3246640
## IC04_IC11                  51.7317298
## IC04_IC12                   0.4726053
## IC05_IC06                   0.8804839
## IC05_IC19                   0.6811388
## IC07_IC13                   0.7290715
## IC08_IC11                   0.1869120
## IC08_IC20                   0.7040206
## IC11_IC12                   0.2743429
## IC12_IC17                   0.4808452
## IC12_IC20                   0.8014064
## IC13_IC14                   0.6868982
## IC14_IC16                   0.5094492
## IC14_IC18                   2.1857454
## IC14_IC20                   0.7359131
## IC15_IC17                   0.7841706
## IC17_IC18                   0.4343160
## IC18_IC19                   0.7588176
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask5 = aovres$SCcorr.repBF>=10
mask6 = aovres$RRBcorr.repBF>=10
mask7 = aovres$SumSCRRB.repBF>=10
mask8 = aovres$zds.repBF>=10
mask9 = aovres$zds_SCequalRRB.repBF>=10
mask10 = aovres$zds_SCoverRRB.repBF>=10
mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
mask12 = aovres$VinelandABC.repBF>=10
mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF",
                    "SCcorr_Disc.r","SCcorr_Rep.r","SCcorr_Disc.pval","SCcorr_Rep.pval","SCcorr.repBF",
                    "RRBcorr_Disc.r","RRBcorr_Rep.r","RRBcorr_Disc.pval","RRBcorr_Rep.pval","RRBcorr.repBF",
                    "SumSCRRB_Disc.r","SumSCRRB_Rep.r","SumSCRRB_Disc.pval","SumSCRRB_Rep.pval","SumSCRRB.repBF",
                    "zds_Disc.r","zds_Rep.r","zds_Disc.pval","zds_Rep.pval","zds.repBF",
                    "SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Rep.r",
                    "SumSCRRB_SCequalRRB_Disc.pval","SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
                    "zds_SCequalRRB_Disc.r","zds_SCequalRRB_Rep.r",
                    "zds_SCequalRRB_Disc.pval","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
                    "zds_SCoverRRB_Disc.r","zds_SCoverRRB_Rep.r",
                    "zds_SCoverRRB_Disc.pval","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
                    "VinelandABC_Disc.r","VinelandABC_Rep.r",
                    "VinelandABC_Disc.pval","VinelandABC_Rep.pval","VinelandABC.repBF",
                    "VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Rep.r",
                    "VinelandABC_SCequalRRB_Disc.pval","VinelandABC_SCequalRRB_Rep.pval",
                    "VinelandABC_SCequalRRB.repBF",
                    "VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Rep.r",
                    "VinelandABC_SCoverRRB_Disc.pval","VinelandABC_SCoverRRB_Rep.pval",
                    "VinelandABC_SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF SCcorr_Disc.r SCcorr_Rep.r
## IC01_IC12 IC01_IC12        0.7636743       0.4923188   0.021941964  0.003233085
## IC03_IC12 IC03_IC12       16.6928467       1.7845001   0.122208052 -0.033982102
## IC03_IC13 IC03_IC13       11.6387970       9.6795040  -0.215653028 -0.126963611
## IC03_IC18 IC03_IC18        0.6298486       1.1861277   0.002136498 -0.153791633
## IC04_IC06 IC04_IC06        0.6636211       0.5358113  -0.149270513  0.161738712
## IC04_IC11 IC04_IC11        0.7585735       0.7054349   0.053684900 -0.018351087
## IC04_IC12 IC04_IC12       16.2382806       0.3722300  -0.043082620  0.008786831
## IC05_IC06 IC05_IC06       81.9293437       1.4657777  -0.027760400  0.009072133
## IC05_IC19 IC05_IC19        0.9141969       1.5631620   0.089447081 -0.036191267
## IC07_IC13 IC07_IC13      390.8687559      19.4748090   0.028251426  0.056376570
## IC08_IC11 IC08_IC11        0.7797743       0.7331277   0.116140332  0.187959425
## IC08_IC20 IC08_IC20        1.3733856       0.7193256   0.061020958  0.200556463
## IC11_IC12 IC11_IC12        0.7316592      10.3433042  -0.097223948 -0.124975082
## IC12_IC17 IC12_IC17        3.2894374      23.7664845  -0.098155586 -0.203831211
## IC12_IC20 IC12_IC20        0.7625460       1.2533726   0.079352256  0.219000361
## IC13_IC14 IC13_IC14      203.6196044       0.5068066  -0.059875454 -0.016340357
## IC14_IC16 IC14_IC16       12.0374592       2.3981289  -0.131419580  0.065191885
## IC14_IC18 IC14_IC18        1.2864757       1.3938615   0.082481691  0.069255232
## IC14_IC20 IC14_IC20        9.5636043       3.2059464   0.109434809 -0.034469408
## IC15_IC17 IC15_IC17        0.4468222       0.3242601   0.039269703 -0.068463813
## IC17_IC18 IC17_IC18       13.6220191       1.7467207   0.127776852 -0.097275106
## IC18_IC19 IC18_IC19        1.5334740      12.0366791  -0.012813809 -0.028593224
##           SCcorr_Disc.pval SCcorr_Rep.pval SCcorr.repBF RRBcorr_Disc.r
## IC01_IC12       0.67039832      0.87737433    0.6954284    0.022253885
## IC03_IC12       0.33308334      0.62869809    0.4613062   -0.052197602
## IC03_IC13       0.03345810      0.26358581    0.9918510   -0.100984042
## IC03_IC18       0.74840893      0.12681306    0.9684752    0.087762907
## IC04_IC06       0.08777141      0.09581035    0.1627004   -0.088447737
## IC04_IC11       0.33780635      0.85020459    0.5096450    0.001223994
## IC04_IC12       0.74868464      0.65673363    0.6674887   -0.018878034
## IC05_IC06       0.58586694      0.88893298    0.6776660    0.129883126
## IC05_IC19       0.30181386      0.73140964    0.4593306    0.111258537
## IC07_IC13       0.79557911      0.78982547    0.7256737    0.006504303
## IC08_IC11       0.11020834      0.02476101    8.0866009    0.140225485
## IC08_IC20       0.62977061      0.04189683    3.1215669    0.004305451
## IC11_IC12       0.34044681      0.21671088    1.4854833   -0.065262479
## IC12_IC17       0.26296847      0.02214415    7.0481993    0.268013929
## IC12_IC20       0.65142557      0.02996203    3.6324648    0.007883081
## IC13_IC14       0.68993704      0.93721109    0.6630706   -0.033549747
## IC14_IC16       0.18534161      0.32510632    0.2967348   -0.020705032
## IC14_IC18       0.27886889      0.32210933    1.1442546    0.052195690
## IC14_IC20       0.46732477      0.50302870    0.5359185    0.059448943
## IC15_IC17       0.93645920      0.28002795    0.9042265    0.059630717
## IC17_IC18       0.28318260      0.23107323    0.3945256    0.066930613
## IC18_IC19       0.57696224      0.60085236    0.8030510    0.078735341
##           RRBcorr_Rep.r RRBcorr_Disc.pval RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12  -0.031570729        0.67581534      0.662192433     0.7712664
## IC03_IC12  -0.268313828        0.53832312      0.009197724     8.0245238
## IC03_IC13  -0.105545423        0.32717839      0.330239493     1.1285575
## IC03_IC18  -0.191165725        0.28544500      0.120789313     0.4190989
## IC04_IC06   0.131186457        0.25946725      0.243491459     0.3703527
## IC04_IC11  -0.006080479        0.39683381      0.489930085     0.8848918
## IC04_IC12  -0.132022831        0.91914190      0.201782973     1.1339116
## IC05_IC06   0.008273737        0.30770101      0.990641170     0.5392303
## IC05_IC19   0.020045801        0.23452312      0.904463122     0.5252455
## IC07_IC13   0.080865498        0.85975739      0.704722665     0.7459577
## IC08_IC11   0.234578549        0.08988736      0.022725465     8.9081085
## IC08_IC20   0.135383657        0.80035822      0.252352808     0.8330652
## IC11_IC12  -0.029488716        0.54328656      0.571609043     0.8222671
## IC12_IC17  -0.111746204        0.03543136      0.471909306     0.1178531
## IC12_IC20   0.291944708        0.52286861      0.011605625     1.4242822
## IC13_IC14   0.240763309        0.94254345      0.005337477     4.5492368
## IC14_IC16   0.131510531        0.89989062      0.128004284     1.1475421
## IC14_IC18   0.206087538        0.34202584      0.015003756     8.0998250
## IC14_IC20  -0.196980403        0.89606118      0.038726525     1.8179576
## IC15_IC17   0.181375760        0.80383217      0.131115427     1.4923754
## IC17_IC18  -0.131431759        0.82941025      0.227572306     0.8816555
## IC18_IC19  -0.085669113        0.90785978      0.147221837     1.1002032
##           SumSCRRB_Disc.r SumSCRRB_Rep.r SumSCRRB_Disc.pval SumSCRRB_Rep.pval
## IC01_IC12      0.02901956    -0.01216495         0.59639342       0.744267295
## IC03_IC12      0.05598002    -0.14992126         0.82324983       0.109489038
## IC03_IC13     -0.21386179    -0.14306998         0.05019802       0.205371003
## IC03_IC18      0.05422217    -0.20242756         0.37914842       0.070358059
## IC04_IC06     -0.15934662     0.18064959         0.06770220       0.080738597
## IC04_IC11      0.03906505    -0.01646374         0.27528630       0.921735250
## IC04_IC12     -0.04208466    -0.05450550         0.78758276       0.723083828
## IC05_IC06      0.05825782     0.01057013         0.82961723       0.926953069
## IC05_IC19      0.13048683    -0.01768556         0.18012260       0.853706029
## IC07_IC13      0.02407416     0.07926642         0.78434455       0.712548753
## IC08_IC11      0.16676443     0.24768499         0.04649155       0.007703423
## IC08_IC20      0.04615055     0.21130671         0.86148346       0.044697063
## IC11_IC12     -0.10848355    -0.10658917         0.32474187       0.261342137
## IC12_IC17      0.09204190    -0.20289733         0.63770172       0.055301730
## IC12_IC20      0.06138142     0.29703865         0.96451992       0.005006840
## IC13_IC14     -0.06286582     0.09966974         0.75457845       0.144709887
## IC14_IC16     -0.10620909     0.10922603         0.37770170       0.155192719
## IC14_IC18      0.09015854     0.14686474         0.18671367       0.054532693
## IC14_IC20      0.11370131    -0.11684976         0.59080811       0.141479102
## IC15_IC17      0.06380923     0.03307955         0.82805483       0.970847557
## IC17_IC18      0.13122882    -0.13299202         0.38186762       0.159515783
## IC18_IC19      0.03812920    -0.06083131         0.75817758       0.260814123
##           SumSCRRB.repBF   zds_Disc.r    zds_Rep.r zds_Disc.pval zds_Rep.pval
## IC01_IC12      0.7303170  0.002657793  0.023309478   0.978621128   0.87424447
## IC03_IC12      1.1153558  0.136402752  0.136734419   0.155024347   0.14857512
## IC03_IC13      1.3712965 -0.107756293 -0.062880426   0.320029498   0.66763968
## IC03_IC18      0.5953742 -0.058854280 -0.035681414   0.570051184   0.66235397
## IC04_IC06      0.1326764 -0.061618574  0.082200387   0.551763355   0.36315105
## IC04_IC11      0.5455604  0.043233647 -0.014899531   0.876162830   0.55813481
## IC04_IC12      0.7448749 -0.022345781  0.092781160   0.845957187   0.15265506
## IC05_IC06      0.6863482 -0.112569212  0.004027948   0.193913203   0.88122595
## IC05_IC19      0.3926020 -0.003416208 -0.049681652   0.908621815   0.65702061
## IC07_IC13      0.7484291  0.018705713  0.006338976   0.934075525   0.99565767
## IC08_IC11     22.7219001 -0.001563469  0.042788914   0.967658120   0.52097367
## IC08_IC20      2.3252346  0.047133160  0.119371439   0.543994106   0.19797173
## IC11_IC12      1.3193817 -0.034806562 -0.109081175   0.735765943   0.38135921
## IC12_IC17      1.0775135 -0.266888039 -0.137767387   0.005822396   0.06242402
## IC12_IC20      4.8818904  0.059725308  0.037417649   0.428559577   0.51946125
## IC13_IC14      0.9384866 -0.026012431 -0.170165849   0.778505391   0.08031152
## IC14_IC16      0.5141249 -0.093700405 -0.016876321   0.277971435   0.91213814
## IC14_IC18      4.1581081  0.031713651 -0.060486527   0.824892929   0.53322281
## IC14_IC20      0.7598084  0.048866197  0.090168120   0.588162711   0.39621295
## IC15_IC17      0.6895181 -0.008920903 -0.185111864   0.909308101   0.03988013
## IC17_IC18      0.5153622  0.058791490 -0.015996310   0.462311380   0.72393577
## IC18_IC19      1.1256414 -0.064881831  0.025111679   0.578426142   0.67263808
##           zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12 0.7031229                 0.08453430              -0.051429808
## IC03_IC12 2.0059578                -0.01155316              -0.252101805
## IC03_IC13 0.7063224                -0.22792682              -0.070945668
## IC03_IC18 0.7661179                -0.14515754              -0.346254372
## IC04_IC06 0.6019329                -0.23983031               0.192596559
## IC04_IC11 0.7263378                 0.07979150              -0.068780809
## IC04_IC12 1.0215623                 0.00970710              -0.216566499
## IC05_IC06 0.5024849                 0.08476974              -0.044465200
## IC05_IC19 0.7523901                 0.26829860              -0.002427627
## IC07_IC13 0.6984898                -0.06478857               0.133901003
## IC08_IC11 0.7679910                 0.20314909               0.237543592
## IC08_IC20 1.4464655                 0.10572511               0.226051761
## IC11_IC12 0.9605286                -0.16341962              -0.217659882
## IC12_IC17 3.1878337                 0.09933078              -0.232918057
## IC12_IC20 0.8578478                 0.17634246               0.385294634
## IC13_IC14 1.9240908                -0.21411146               0.330555609
## IC14_IC16 0.5499091                -0.08390345               0.159239090
## IC14_IC18 0.7130210                 0.08960937               0.244718725
## IC14_IC20 0.9848591                 0.08161356              -0.220639840
## IC15_IC17 2.3174567                 0.04856125               0.149623778
## IC17_IC18 0.5522379                 0.09205547              -0.137009332
## IC18_IC19 0.6018958                 0.09847882              -0.128455963
##           SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12                    0.56188888                  0.974935179
## IC03_IC12                    0.93872894                  0.035689420
## IC03_IC13                    0.10700501                  0.570160190
## IC03_IC18                    0.45189173                  0.005517810
## IC04_IC06                    0.03046698                  0.165815584
## IC04_IC11                    0.31151345                  0.836345746
## IC04_IC12                    0.94603287                  0.110996817
## IC05_IC06                    0.87292946                  0.714012828
## IC05_IC19                    0.08498255                  0.963383500
## IC07_IC13                    0.75442822                  0.266238495
## IC08_IC11                    0.04208890                  0.071784762
## IC08_IC20                    0.60674866                  0.111946658
## IC11_IC12                    0.24441293                  0.108452055
## IC12_IC17                    0.43803105                  0.140246358
## IC12_IC20                    0.43404386                  0.003877243
## IC13_IC14                    0.25593092                  0.004580470
## IC14_IC16                    0.28336103                  0.227197717
## IC14_IC18                    0.26456122                  0.012195743
## IC14_IC20                    0.82365218                  0.090297943
## IC15_IC17                    0.83349736                  0.323829438
## IC17_IC18                    0.99329924                  0.405920958
## IC18_IC19                    0.93109548                  0.292075276
##           SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Rep.r
## IC01_IC12                0.63757610          -0.304764964          0.007186694
## IC03_IC12                2.38392316           0.053429273          0.238823062
## IC03_IC13                0.61429155           0.056651156         -0.048901495
## IC03_IC18               13.08618251          -0.101232305         -0.034137618
## IC04_IC06                0.07384865          -0.108473930          0.009091615
## IC04_IC11                0.48880187          -0.082045454          0.029230550
## IC04_IC12                1.42618135           0.043130364          0.019854650
## IC05_IC06                0.69969425          -0.062024451          0.046155169
## IC05_IC19                0.30896018           0.059003703          0.024552826
## IC07_IC13                0.78795137          -0.136142503          0.059794448
## IC08_IC11                3.58418088          -0.034850679          0.045497331
## IC08_IC20                1.90443486           0.181212224         -0.043494587
## IC11_IC12                2.48406719           0.084597673          0.061218318
## IC12_IC17                0.59018732          -0.150194639         -0.235150588
## IC12_IC20               16.66623696          -0.042696936         -0.139094881
## IC13_IC14                0.79291236          -0.315130289          0.004083003
## IC14_IC16                0.39291048          -0.158107047          0.002437298
## IC14_IC18               10.85032508          -0.255930003         -0.032115490
## IC14_IC20                1.20057044           0.080616740          0.106186082
## IC15_IC17                0.98902056          -0.019421667         -0.321794127
## IC17_IC18                0.83935203           0.065140888         -0.150301757
## IC18_IC19                0.88977031          -0.000418521          0.136700449
##           zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF
## IC01_IC12              0.010689632             0.878332731           0.15178244
## IC03_IC12              0.669109307             0.044852452           2.92923690
## IC03_IC13              0.535414365             0.777230467           0.59291183
## IC03_IC18              0.456688706             0.769581356           0.69309880
## IC04_IC06              0.523002532             0.928086526           0.61291571
## IC04_IC11              0.271810462             0.974263725           0.50296076
## IC04_IC12              0.631124715             0.735180922           0.73826492
## IC05_IC06              0.755680619             0.767550870           0.66639479
## IC05_IC19              0.723850643             0.788327041           0.72427852
## IC07_IC13              0.208234888             0.725260931           0.38410749
## IC08_IC11              0.434726334             0.554933175           0.51876484
## IC08_IC20              0.041248059             0.733317305           0.16871633
## IC11_IC12              0.536583057             0.456206960           0.92429365
## IC12_IC17              0.302068917             0.029547179           5.72240258
## IC12_IC20              0.974005398             0.354297525           0.88759179
## IC13_IC14              0.004342648             0.978347811           0.07565999
## IC14_IC16              0.305900575             0.990160505           0.52988358
## IC14_IC18              0.022048419             0.717548856           0.27604698
## IC14_IC20              0.470824746             0.425928307           0.96437838
## IC15_IC17              0.810102206             0.003769356           8.76381163
## IC17_IC18              0.259002731             0.134704708           0.38382072
## IC18_IC19              0.732690251             0.208950037           1.27274777
##           zds_SCoverRRB_Disc.r zds_SCoverRRB_Rep.r zds_SCoverRRB_Disc.pval
## IC01_IC12           0.12514764         -0.01383797             0.457925466
## IC03_IC12           0.31483942         -0.09835797             0.038041964
## IC03_IC13          -0.35132901         -0.05521216             0.037758465
## IC03_IC18           0.15002844          0.07306033             0.418197561
## IC04_IC06           0.16360124         -0.02879048             0.436984271
## IC04_IC11           0.18858525          0.03255199             0.137162322
## IC04_IC12          -0.09421679         -0.02818725             0.883365684
## IC05_IC06          -0.11190641          0.05835806             0.539267142
## IC05_IC19          -0.22911017         -0.04303200             0.241649389
## IC07_IC13           0.49912811         -0.04158595             0.001538571
## IC08_IC11          -0.27380487         -0.22139803             0.100054802
## IC08_IC20           0.07577193          0.10609126             0.856389971
## IC11_IC12          -0.15366924         -0.06426293             0.505381795
## IC12_IC17          -0.48501988         -0.07174130             0.002428651
## IC12_IC20           0.27712226          0.17403741             0.165586954
## IC13_IC14           0.01863420         -0.26895060             0.895098975
## IC14_IC16          -0.32092795         -0.08336991             0.064032091
## IC14_IC18          -0.07593056         -0.16472460             0.732498079
## IC14_IC20           0.22447372         -0.06365687             0.151820584
## IC15_IC17          -0.22142959         -0.44367638             0.075975895
## IC17_IC18           0.17504276         -0.08765311             0.310036782
## IC18_IC19           0.16724776          0.04113726             0.350918942
##           zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF VinelandABC_Disc.r
## IC01_IC12            0.883286514          0.57697056        0.117928538
## IC03_IC12            0.512752442          0.12125287       -0.072641630
## IC03_IC13            0.667595070          0.36356118       -0.134772608
## IC03_IC18            0.682208585          0.73059618       -0.061046059
## IC04_IC06            0.887113396          0.56890841        0.071190982
## IC04_IC11            0.854583787          0.45261707       -0.103254277
## IC04_IC12            0.870999053          0.70866468        0.002441248
## IC05_IC06            0.664033284          0.58228054        0.057668657
## IC05_IC19            0.769214412          0.59585482       -0.143747879
## IC07_IC13            0.888895622          0.03157664        0.073694498
## IC08_IC11            0.129081609          2.28966778       -0.041406876
## IC08_IC20            0.499225579          0.83225718       -0.012128605
## IC11_IC12            0.710768595          0.73294943        0.056106901
## IC12_IC17            0.599942240          0.12966061        0.016795507
## IC12_IC20            0.252394877          1.34700523        0.027880754
## IC13_IC14            0.086989107          1.69900539        0.025951362
## IC14_IC16            0.586797138          0.50748612        0.202370386
## IC14_IC18            0.307540628          1.06590557        0.118454122
## IC14_IC20            0.761341021          0.33330483       -0.182685897
## IC15_IC17            0.003992099         44.91523696       -0.025714298
## IC17_IC18            0.608602301          0.43959511       -0.162183284
## IC18_IC19            0.770715661          0.65484908       -0.095770335
##           VinelandABC_Rep.r VinelandABC_Disc.pval VinelandABC_Rep.pval
## IC01_IC12       0.177064715            0.20008779          0.067791395
## IC03_IC12       0.007354335            0.36854945          0.976017651
## IC03_IC13       0.075485086            0.22800372          0.320234088
## IC03_IC18      -0.002086721            0.68570212          0.950347592
## IC04_IC06      -0.250785296            0.45632415          0.005964628
## IC04_IC11      -0.112075279            0.23611656          0.211706170
## IC04_IC12      -0.131798765            0.92509074          0.222921592
## IC05_IC06       0.039613563            0.73340254          0.756818969
## IC05_IC19      -0.211936396            0.13287524          0.026487633
## IC07_IC13      -0.046360656            0.52157295          0.522445833
## IC08_IC11       0.007092690            0.77280316          0.764259781
## IC08_IC20      -0.214624658            0.95294781          0.017210910
## IC11_IC12       0.056210538            0.55274527          0.479505554
## IC12_IC17       0.053290790            0.82865168          0.705244219
## IC12_IC20      -0.077436310            0.82065386          0.423044058
## IC13_IC14       0.026756794            0.68294229          0.731917061
## IC14_IC16      -0.075385606            0.04684481          0.505927395
## IC14_IC18       0.062231634            0.22321958          0.441414333
## IC14_IC20      -0.218013422            0.04074162          0.011970586
## IC15_IC17      -0.097459593            0.65371858          0.231414244
## IC17_IC18      -0.065270821            0.08848605          0.383315323
## IC18_IC19       0.141866569            0.26986009          0.131402514
##           VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12         3.5207973                  0.1067004255
## IC03_IC12         0.5607317                 -0.1006967711
## IC03_IC13         0.3406041                 -0.2073287286
## IC03_IC18         0.6802684                  0.0144610042
## IC04_IC06         1.4756638                 -0.0568329390
## IC04_IC11         1.5367022                 -0.1012964197
## IC04_IC12         0.9670556                 -0.1555691202
## IC05_IC06         0.7353533                  0.1376289705
## IC05_IC19         7.4441019                 -0.2372982915
## IC07_IC13         0.5695473                  0.1002723097
## IC08_IC11         0.6711232                 -0.3478406641
## IC08_IC20         3.2002400                 -0.1796873079
## IC11_IC12         0.8989774                 -0.1793628342
## IC12_IC17         0.7492798                 -0.1076469471
## IC12_IC20         0.7447499                 -0.0006189235
## IC13_IC14         0.7423939                  0.0191741885
## IC14_IC16         0.1446152                  0.2976287607
## IC14_IC18         0.8937073                  0.1247968842
## IC14_IC20        16.1666432                 -0.2929031944
## IC15_IC17         1.2606998                  0.0579360643
## IC17_IC18         0.8498884                 -0.0794348038
## IC18_IC19         0.3986396                 -0.1249865158
##           VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12                 -0.028225551                      0.474313869
## IC03_IC12                 -0.062394517                      0.460526571
## IC03_IC13                  0.028136233                      0.174620368
## IC03_IC18                  0.081908225                      0.639641254
## IC04_IC06                 -0.378369363                      0.618756230
## IC04_IC11                  0.203348437                      0.392475830
## IC04_IC12                 -0.129102367                      0.286922336
## IC05_IC06                  0.196632228                      0.428014151
## IC05_IC19                 -0.343753200                      0.056085994
## IC07_IC13                 -0.129714056                      0.573378368
## IC08_IC11                  0.109095820                      0.006745789
## IC08_IC20                 -0.380912406                      0.232980570
## IC11_IC12                  0.053705671                      0.177836017
## IC12_IC17                  0.100371752                      0.612022813
## IC12_IC20                 -0.003099587                      0.843525917
## IC13_IC14                  0.043940453                      0.785993679
## IC14_IC16                 -0.045257523                      0.028797530
## IC14_IC18                  0.008059356                      0.341305658
## IC14_IC20                 -0.294362907                      0.022108601
## IC15_IC17                 -0.106195933                      0.868268122
## IC17_IC18                 -0.093917095                      0.557881568
## IC18_IC19                  0.157557469                      0.253719517
##           VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12                     0.751955781                   0.56125646
## IC03_IC12                     0.503428338                   0.87585412
## IC03_IC13                     0.672151604                   0.33929349
## IC03_IC18                     0.556072065                   0.83252710
## IC04_IC06                     0.001184757                  23.41869444
## IC04_IC11                     0.125166136                   0.54954983
## IC04_IC12                     0.429535418                   0.93927505
## IC05_IC06                     0.142103918                   1.87510553
## IC05_IC19                     0.004279808                  38.01376662
## IC07_IC13                     0.234370431                   0.66420106
## IC08_IC11                     0.241339263                   0.02836349
## IC08_IC20                     0.001022701                  61.02756904
## IC11_IC12                     0.437243122                   0.30097777
## IC12_IC17                     0.459897734                   0.62415799
## IC12_IC20                     0.988957004                   0.69163219
## IC13_IC14                     0.553336609                   0.81526491
## IC14_IC16                     0.733278256                   0.14004165
## IC14_IC18                     0.833248475                   0.61992832
## IC14_IC20                     0.010024113                  20.86007442
## IC15_IC17                     0.223450591                   0.92236744
## IC17_IC18                     0.351932765                   1.05624652
## IC18_IC19                     0.191276991                   0.36574951
##           VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Rep.r
## IC01_IC12                   0.17450773                 0.536418218
## IC03_IC12                  -0.05829850                 0.165714100
## IC03_IC13                  -0.05493026                 0.076907406
## IC03_IC18                  -0.13645941                -0.176766966
## IC04_IC06                   0.23460265                -0.026735180
## IC04_IC11                  -0.10925131                -0.488919436
## IC04_IC12                   0.15073623                -0.163987447
## IC05_IC06                  -0.01252665                -0.131175026
## IC05_IC19                  -0.05625207                -0.023597413
## IC07_IC13                   0.06692883                 0.055654422
## IC08_IC11                   0.24185171                -0.158762892
## IC08_IC20                   0.12353340                 0.067336298
## IC11_IC12                   0.30720503                 0.005041906
## IC12_IC17                   0.14696804                -0.096416357
## IC12_IC20                   0.06309952                -0.193767438
## IC13_IC14                   0.01091215                -0.012687804
## IC14_IC16                   0.11380876                -0.107986615
## IC14_IC18                   0.07760485                 0.260483377
## IC14_IC20                  -0.06185320                -0.046077450
## IC15_IC17                  -0.11811210                -0.077664969
## IC17_IC18                  -0.27698340                -0.042239434
## IC18_IC19                  -0.04723721                 0.126906127
##           VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12                      0.25646580                   0.0003763654
## IC03_IC12                      0.58902280                   0.2862861949
## IC03_IC13                      0.77979061                   0.5361854701
## IC03_IC18                      0.44624818                   0.3135064402
## IC04_IC06                      0.13348894                   0.8073516501
## IC04_IC11                      0.42462069                   0.0009068565
## IC04_IC12                      0.37583455                   0.3097789492
## IC05_IC06                      0.91621952                   0.3970113293
## IC05_IC19                      0.75496364                   0.9903664593
## IC07_IC13                      0.77300968                   0.7772829569
## IC08_IC11                      0.09388985                   0.3548774338
## IC08_IC20                      0.46001880                   0.7488366371
## IC11_IC12                      0.04726877                   0.9085844864
## IC12_IC17                      0.36645122                   0.6246726416
## IC12_IC20                      0.65746863                   0.1681895928
## IC13_IC14                      0.83088998                   0.9295495843
## IC14_IC16                      0.42424047                   0.5332027590
## IC14_IC18                      0.58348099                   0.0935084324
## IC14_IC20                      0.59893938                   0.7317663965
## IC15_IC17                      0.52374645                   0.6277929619
## IC17_IC18                      0.06414327                   0.6793765600
## IC18_IC19                      0.84327532                   0.4175585985
##           VinelandABC_SCoverRRB.repBF
## IC01_IC12                 152.6207509
## IC03_IC12                   0.6511909
## IC03_IC13                   0.6945975
## IC03_IC18                   1.1612551
## IC04_IC06                   0.3246640
## IC04_IC11                  51.7317298
## IC04_IC12                   0.4726053
## IC05_IC06                   0.8804839
## IC05_IC19                   0.6811388
## IC07_IC13                   0.7290715
## IC08_IC11                   0.1869120
## IC08_IC20                   0.7040206
## IC11_IC12                   0.2743429
## IC12_IC17                   0.4808452
## IC12_IC20                   0.8014064
## IC13_IC14                   0.6868982
## IC14_IC16                   0.5094492
## IC14_IC18                   2.1857454
## IC14_IC20                   0.7359131
## IC15_IC17                   0.7841706
## IC17_IC18                   0.4343160
## IC18_IC19                   0.7588176
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

SCcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Disc_mat) = comps
colnames(SCcorr_Disc_mat) = comps
diag(SCcorr_Disc_mat) = 0

SCcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Rep_mat) = comps
colnames(SCcorr_Rep_mat) = comps
diag(SCcorr_Rep_mat) = 0

RRBcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Disc_mat) = comps
colnames(RRBcorr_Disc_mat) = comps
diag(RRBcorr_Disc_mat) = 0

RRBcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Rep_mat) = comps
colnames(RRBcorr_Rep_mat) = comps
diag(RRBcorr_Rep_mat) = 0

SumSCRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Disc_mat) = comps
colnames(SumSCRRB_Disc_mat) = comps
diag(SumSCRRB_Disc_mat) = 0

SumSCRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Rep_mat) = comps
colnames(SumSCRRB_Rep_mat) = comps
diag(SumSCRRB_Rep_mat) = 0

zds_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Disc_mat) = comps
colnames(zds_Disc_mat) = comps
diag(zds_Disc_mat) = 0

zds_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Rep_mat) = comps
colnames(zds_Rep_mat) = comps
diag(zds_Rep_mat) = 0


zds_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Disc_mat) = comps
colnames(zds_SCequalRRB_Disc_mat) = comps
diag(zds_SCequalRRB_Disc_mat) = 0

zds_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Rep_mat) = comps
colnames(zds_SCequalRRB_Rep_mat) = comps
diag(zds_SCequalRRB_Rep_mat) = 0

zds_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Disc_mat) = comps
colnames(zds_SCoverRRB_Disc_mat) = comps
diag(zds_SCoverRRB_Disc_mat) = 0

zds_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Rep_mat) = comps
colnames(zds_SCoverRRB_Rep_mat) = comps
diag(zds_SCoverRRB_Rep_mat) = 0




VinelandABC_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Disc_mat) = comps
colnames(VinelandABC_Disc_mat) = comps
diag(VinelandABC_Disc_mat) = 0

VinelandABC_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Rep_mat) = comps
colnames(VinelandABC_Rep_mat) = comps
diag(VinelandABC_Rep_mat) = 0


VinelandABC_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Disc_mat) = comps
colnames(VinelandABC_SCequalRRB_Disc_mat) = comps
diag(VinelandABC_SCequalRRB_Disc_mat) = 0

VinelandABC_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Rep_mat) = comps
colnames(VinelandABC_SCequalRRB_Rep_mat) = comps
diag(VinelandABC_SCequalRRB_Rep_mat) = 0

VinelandABC_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Disc_mat) = comps
colnames(VinelandABC_SCoverRRB_Disc_mat) = comps
diag(VinelandABC_SCoverRRB_Disc_mat) = 0

VinelandABC_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Rep_mat) = comps
colnames(VinelandABC_SCoverRRB_Rep_mat) = comps
diag(VinelandABC_SCoverRRB_Rep_mat) = 0



for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCcorr.repBF"]>10 & 
      aovres[comp_pair,"SCcorr_Disc.pval"]<0.05 & 
      aovres[comp_pair,"SCcorr_Rep.pval"]<0.05){
    SCcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Disc.r"]
    SCcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Rep.r"]
  } else{
    SCcorr_Disc_mat[comp1,comp2] = 0.0001
    SCcorr_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"RRBcorr.repBF"]>10 & 
      aovres[comp_pair,"RRBcorr_Disc.pval"]<0.05 & 
      aovres[comp_pair,"RRBcorr_Rep.pval"]<0.05){
    RRBcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Disc.r"]
    RRBcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Rep.r"]
  } else{
    RRBcorr_Disc_mat[comp1,comp2] = 0.0001
    RRBcorr_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"SumSCRRB.repBF"]>10 & 
      aovres[comp_pair,"SumSCRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"SumSCRRB_Rep.pval"]<0.05){
    SumSCRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Disc.r"]
    SumSCRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Rep.r"]
  } else{
    SumSCRRB_Disc_mat[comp1,comp2] = 0.0001
    SumSCRRB_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"zds.repBF"]>10 & 
      aovres[comp_pair,"zds_Disc.pval"]<0.05 & 
      aovres[comp_pair,"zds_Rep.pval"]<0.05){
    zds_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_Disc.r"]
    zds_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_Rep.r"]
  } else{
    zds_Disc_mat[comp1,comp2] = 0.0001
    zds_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"zds_SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"zds_SCequalRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"zds_SCequalRRB_Rep.pval"]<0.05){
    zds_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Disc.r"]
    zds_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Rep.r"]
  } else{
    zds_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    zds_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"zds_SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"zds_SCoverRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"zds_SCoverRRB_Rep.pval"]<0.05){
    zds_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Disc.r"]
    zds_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Rep.r"]
  } else{
    zds_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    zds_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"VinelandABC.repBF"]>10 & 
      aovres[comp_pair,"VinelandABC_Disc.pval"]<0.05 & 
      aovres[comp_pair,"VinelandABC_Rep.pval"]<0.05){
    VinelandABC_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Disc.r"]
    VinelandABC_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Rep.r"]
  } else{
    VinelandABC_Disc_mat[comp1,comp2] = 0.0001
    VinelandABC_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"VinelandABC_SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.pval"]<0.05){
    VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.r"]
    VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.r"]
  } else{
    VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"VinelandABC_SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.pval"]<0.05){
    VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.r"]
    VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.r"]
  } else{
    VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
      
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))



col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))








col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))



plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

#------------------------------------------------------------------------------
# Consensus Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_consensusPairs = c("IC07_IC13","IC03_IC12")
SCoverRRB_consensusPairs = c("IC12_IC17")

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (is.element(comp_pair,SCequalRRB_consensusPairs)){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  } # if
  
  if (is.element(comp_pair,SCoverRRB_consensusPairs)){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  } # if

} # for

grid.col = c(IC01 = "grey",
             IC03 = "green",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "green", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "green",
             IC13 = "green", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "blue",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "blue",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar